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Object Detection - Science topic

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Questions related to Object Detection
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Hello everyone! I am in search of a suitable dataset for the Yolov5 application. I need a dataset for the object detection task with several kinds of balls and a suitable annotation.
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I used this dataset this dataset for solving object detection task: https://www.kaggle.com/datasets/mlwhiz/detection-footballvscricketball
Thank you all, colleagues!
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Does anyone get errors with using the Recognition part in " Scene Text Detection and Recognition by CRAFT and a Four-Stage Network" ?
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etecting and Recognizing texts for a given natural scene image using EAST and Tesseract Algorithms.
Regards,
Shafagat
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I have carried out a work on object detection specifically to detection of pedestrians using YOLO v1, v2, v3, v4 and tiny YOLO v1, v2, v3 and v4 algorithm and proposed a new variant of YOLO algorithm. The employed and proposed variants of YOLO have been evaluated using precision, recall, f1 score, AP for each class and map value.
I submitted the same work to a journal and in response reviewer has asked for carrying out statistical analysis using Friedman Aligned Rank Test, Wilcoxon Test, Quade Test etc and find out the P value.
Is it possible to apply such tests on an object detection problem? Here the training and testing have been done on images. How to apply such statistical tests. Please guide.
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Who or what decides TP or FP?
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I have carried out a work on object detection specifically to detection of pedestrians using YOLO v1, v2, v3, v4 and tiny YOLO v1, v2, v3 and v4 algorithm and proposed a new variant of YOLO algorithm. The employed and proposed variants of YOLO have been evaluated using precision, recall, f1 score, AP for each class and map value.
I submitted the same work to a journal and in response reviewer has asked for carrying out statistical analysis using Friedman Aligned Rank Test, Wilcoxon Test, Quade Test etc and find out the P value.
Is it possible to apply such tests on an object detection problem? Here the training and testing have been done on images. How to apply such statistical tests. Please guide.
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Akhil Kumar I'm not sure what was the goal of your research and whether you tested the same group of people in different conditions or compared different groups. The choice of statistical method depends on it.
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I and my team would to improve a system with 3 sensors HC-SR04 to detection the obstacles for an autonomous agriculture truck. We use Arduino to implement this.
The question is: With only sensors HC-SR04 is possibile to detect the velocity and the size measurement of the object?
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Thanks for reply but there is a problem: the sensors are installed horizzontally next to each other (50cm to each other). So, i think that it’s impossible use the triangolation.
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EfficientDet outputs classes and bounding boxes. My question is about both but specifically I am interested in the class prediction net part. In the paper's diagram it shows 2 conv layers. I don't understand the code and how it works. And what's the difference between the 2 conv layers of classification and box prediction?
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Hello all,
I am trying to have object detection using point clouds. But the constraint is I cannot use machine learning approach because I do not have enough data to train a network and it is not an usual class object found in the pre-trained networks.
I want to proceed with traditional approach. I have come across segmentation and clustering approaches for obtaining the points that represent the desired object in the 3d point cloud. I have also tried RANSAC to get the plane on which the object lies in order to eliminate outlier points.
I am still looking for any other ideas which can serve the purpose. I would be really grateful if I can find some new ideas.
Thank you very much!
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2D object detection classifies the object category and estimates oriented 2D bounding boxes of physical objects from 3D sensor data.
Regards,
Shafagat
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I have a dataset and I intend to use multi-label learning approach to recognize the various objects present in the images dataset. What is/are the appropriate segmentation approach to use for the multiple objects detection task?
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Multi-label classification involves predicting zero or more class labels. Unlike normal classification tasks where class labels are mutually exclusive, multi-label classification requires specialized machine learning algorithms that support predicting multiple mutually non-exclusive classes or “labels.”
Regards,
Shafagat
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For many smart road applications, objects detection and recognition are one of the most important components. Indeed, precise detection of road objects is a critical task for autonomous urban driving and robotics technologies.
Actually, we are working on the conception of a smart system that consists in detecting and blurring undesirable road objects to anonymize and secure road users.
In this context, we want to know what are the up-to-date methods (Neural approaches, etc.) used for the detection and recognition of road objects (humans, vehicles, license plates, etc.) ? Any ideas ?
Greatly appreciate your review
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Dear Prof. Wissam Kaddah,
I have the following research in this area:
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When training a CNN for object detection within images, it is supplied with images containing examples of said object (or list of objects) through their coordinates within the image.
But what happens if you don't supply the coordinates of all the objects in the training images? So there are some object in your training images that match the pattern that the CNN is looking for but are marked as negatives...
I suspect this has a mostly negative impact but, Are there any resources/papers that go over this issue into details?
Thanks
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ONNX based YOLOv5s Model is running perfectly fine, when applied on images for object detection. But when same model is running in Live stream, objects are detecting but bounding boxes keep on flickering. I want them to be fixed. Can anyone please help me with that? Please give the reason for such flickering and solution for the same?
Thanks in advance!
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I recently supervised a Master thesis with the title "Stabilizing Non-Maximum-Suppression: More Stable Replacement for Non-Maximum-Suppression in Object and Face Detectors" where the author has pointed out that the Non-Maximum Suppression (NMS) algorithm applied in all object and face detectors is the problem that makes bounding boxes jitter substantially.
He found that one algorithm is able to improve this, which is:
Bodla, N., Singh, B., Chellappa, R., and Davis, L. S.: Improving Object Detection With One Line of Code. ICCV 2017
Another algorithm proposed by himself was to take a weighted average of the detected bounding boxes instead of suppressing overlapping boxes. Incorporating such a different version of NMS into object detectors is not that easy, though,
Unfortunately, the thesis is not yet published, but I can send you a version of that privately.
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Thinking about Transfer learning.
Can we learn a few features from model one and few from model two, and make a single model perform both tasks??
> let's suppose person detection from YOLOv3 model_person.weight and another face detection from YOLOv3 model_face.weight, Can we combine and make a single model, detect Person and face as well.
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Hi Chandan,
Did you find the solution for this, I have been working on something similar to this. If you have found a solution for this would you like to share it?
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I need to know do I need to calculate the above metrics by selecting every available trained weight with all the test data to evaluate the model performance? Or do I only need to calculate those metrics for the best weight file I was chosen as my final model for the complete test set? The reason for asking this is in the below reference the author of the repository calculates both metrics by taking every weight file for the complete test set.
Please refer line 152 .
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I need to know whether I should consider the number of labels available or the number of images available to increase the model performance. I mean in my case in a single image I have multiple annotated labels available. I know when I increase the number of images for training the accuracy of the model will go high. But in my case do I need to pay attention to maximizing the number of labels in the dataset or the number of images in the dataset to get better accuracy.
Ex:
Case 1:
1 image contains 4 annotated labels(2 for ClassA , 2 for ClassB)
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Total: 60 images
Case 2:
image_b_1 contains 1 annotated label(1 for ClassA)
image_b_2 contains 1 annotated label(1 for ClassB)
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Total: 200 images
Which case will give the maximum accuracy results during the training?
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Your approach is OK. I would like to suggest you to focus on the model selection (appropriate statistical model) as well. You can gradually enhance the accuracy with more data availability and labeling. Some clustering model, e.g., von Misses Fisher Distribution on unit hypersphere may be one the initial choice.
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I need to calculate the accuracy, precision, recall, specificity, and F1 score for my Mask-RCNN model. Hence I hope to calculate the confusion matrix to the whole dataset first to get the TP, FP, TN, FN values. But I noticed almost all the available solutions out there for the calculation of confusion matrix, only outputs the TP, FP, and FN values. So how can I calculate metrics like accuracy and specificity which includes TN as a parameter?.Should I consider the TN as 0 during the calculations ?
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Hello dear researchers.
I run the siamfc ++ algorithm. It showed better results than the other algorithms but its accuracy is still not acceptable. One of my ideas is to run an object detection algorithm first and then draw a bunding box around the object I want to track.I think by doing this I have indirectly labeled the selected object. So the results and accuracy should be improved compared to the previous case. Do you think this is correct? Is there a better idea to improve the algorithm?
Thanks for your tips
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Your welcome Shahrzad Khalifeh Mehrjardi, Yes Precisely.
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Hi everybody,
I am training a Tensorflow model that starts out normally, but then proceeds to rapidly increase in loss after about 16,900 training steps. I have written a question on this AI Stack Overflow post:
Would somebody take a look at this question and be able to provide me some feedback as to what could be going on? Do I just need to terminate training at around 16,900 steps, or is there something else that could be going on?
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This is not outside of two things:
1. Incorrect architecture(not enough or necessary layers, loss function, etc)
2. Data(preprocessing, insertion, etc)
So, make sure both are correct. Indeed it's best for you to run the code step by step and see what's going wrong, or what you haven't done.
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I'm using Python 3.9, Tensorflow 2.6.0, Ubuntu 20.04, and following this tutorial. The tf.record files have just been generated, and I'm trying to get my training to start. the .config files were downloaded from the Tensorflow 2 Detection Model Zoo (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md).
While trying to kick off training for my Tensorflow model with the following code...:
python model_main_tf2.py --model_dir=path_to_model_dir/my_ssd_resnet50_v1_fpn --pipeline_config_path=path_to_model_dir/my_ssd_resnet50_v1_fpn/pipeline.config
...I received this error:
tensorflow.python.framework.errors_impl.InvalidArgumentError: assertion failed: [[0.0792563558][0.286692768][0.342465758]] [[0.000978473574][0.365949124][0.695694685]]
This Stack Overflow post (https://stackoverflow.com/questions/62075321/tensorflow-python-framework-errors-impl-invalidargumenterror-invalid-argument) outlines the error nicely, but I'm still needing assistance in solving it. I have checked, and there are no invalid entries (min/max values outside of the photo's dimensions, and no negative values). The most likely culprit is that several of the bounding boxes were drawn in a reverse order so that some of the min bounding box dimensions are larger than the max dimensions, as discussed by the post. I need to correct the bounding box dimensions before generating the tf.record files (for both my training and testing datasets).
I have labeled my images with Labelimg in Pascal VOC format, so each of my images have an XML file with them (I have roughly 10,900 of them). Is there a way to convert my xml files to a CSV document and THEN generate the tf.record files?
Once the CSV files are generated, I can correct the ordering of my min and max columns with the following code. I'm hoping to use these corrected csv files to generate the tf.record files.
#Designed to correctly modify min/max columns in the CSV files generated when producing tf.record files #Designed to run each line from the command line
import pandas import numpy as np import os import csv #Open CV file df = pandas.read_csv("C:\\desired_directory\\train.csv") df = df.assign( xmin=df[['xmin', 'xmax']].min(1), xmax=df[['xmin', 'xmax']].max(1), ymin=df[['ymin', 'ymax']].min(1), ymax=df[['ymin', 'ymax']].max(1), )
df.to_csv("C:\\desired_directory_to_save_csv_file\\train.csv")
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I am training an object detection model, and I have some very highly unbalanced data annotations. I have almost 11,000 images, all with dimensions of 1024 x 1024.
Within those images I have the following number of annotations:
*Class 1 - 40,000
*Class 2 - 25,000
*Class 3 - 900
*Class 4 - 500
This goes on for a few more classes.
As this is an object detection algorithm that was annotated with the annotation tool Label-img, there are often multiple annotations on each photo. Do any of you have any recommendations as to how to handle fine-tuning an object-detection algorithm on an unbalanced dataset? Currently, collecting more imagery is not an option. I would augment the images and re-label, but since there are multiple annotations on the images, I would be increasing the number of annotations for the larger classes as well.
Note: I'm using the Tensorflow Object Detection API and have downloaded the models and .config files from the Tensorflow 2 Detection Model Zoo.
Thanks everybody!
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If you can write your own layer and add it to the layers and then fine tune it would be good. Especially using weighted classes to pay more attention to those underrepresented classes. This means you change the optimization algorithm. Then you need to add your own .config file.
Otherwise, a simple solution is to use metrics that are more suitable for imbalanced datasets, like AUC, F1-score, etc.
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Dear colleagues, I am working on a project of Object detection with Bounding box regression (on Keras, TensorFlow), but I can't find decent sources and code samples. My task involves 200 classes, and each picture can contain from 10 to 20 classes that need to be detected. A medical task, more precisely, the recognition of types of medical blood tests.
I fixed some works that are close to mine. But I want to implement my task for the Russian language and on the KERAS & TensorFlow framework. I am sincerely grateful for any advice and support!
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I have captured the depth image from Intel REALSENSE depth camera , and saved depth image as RGB-D data format . However , I want to detect the change of position of a ball in the picture .Because the image is represented in depth format, it is different from the 2D image . How can i do this ? Thanks !
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Hi Wang!
Hope this will help your work in some aspects:
①https://github.com/topics/rgb-d
③https://paperswithcode.com/task/rgb-d-salient-object-detection#:~:text=RGB-D%20Salient%20object%20detection%20%28SOD%29%20aims%20at%20distinguishing,scene%20from%20the%20given%20RGB%20and%20Depth%20data.
④https://www.cs.washington.edu/research-projects/robotics/rgbd-object-recognition-and-detection/
Kind Regards,
Xiao
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I am interested in working in the research about Object detection or object recognition. I need your help on the field like research proposal and source codes or any links for extra assist or materials for the field. Thanks in advance.
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its depend upon your data and if you are going to do your work in remote sensing then i can help you.
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Hi Everyone,
I'm currently practising an object detection model which should detect a car, person, truck, etc. in both day and night time. Now, I have started gathering data for both day and night time. I'm not sure whether to train a separate model for daylight and another model for the night-light or to combine together and train it?
can anyone suggest to me the data distribution for each class at day and night light? I presume it should be a uniform distribution. Please correct me if I'm wrong.
Eg: for person: 700 images at daylight and another 700 images for nightlight
Any suggestion would be helpful.
Thanks in Advance.
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Ben Harper, thanks very much for your recommendation.
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Do any semi-supervised deep learning technique or architecture exist for object detection ?
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A lot of such methods exist. You can try searching in Google Scholar: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=semi+supervised+object+detection&btnG=
Or if you are looking for implementations as well try Paper With Code: https://paperswithcode.com/search?q_meta=&q_type=&q=semi+supervised+object+detection
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I have been working on object detection with YOLO as my master's thesis. I have done some experiments but trying to understand the architecture but it is getting very difficult to understand the architecture from one source. Can somebody please help me by providing good resources regarding this? I am very grateful to you in advance.
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Hi,
I am using Tensorflow object detection api with Faster-RCNN arcitecture and ResNet152 for training and object detection. Since I do have small number of train/validation images the obtained mAP@0.5 is low (~0.6). Do you know any good documentation, video whatever which describes how to use transfer learning in tensorflow object detection api? I would like to try this to see if this helps to increase the accuracy.
Thanks
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hello,
i want to know how to use the XML files in Object detections datasets !
how to read, how to store information and finally how to use !
any information ! link will it will be helpfull -_-
thx
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I would recommend doing this in python, check out this link: https://stackabuse.com/reading-and-writing-xml-files-in-python/
Cheers, Raoul
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Hello everyone,
I am working on a project which includes object recognition, currency recognition, text to speech, and location of the user. In order to perform the currency recognition of Pakistan's currency, I'd need a lot of data and computing resources to train a model, but unfortunately, I don't have access to either of these.
So, I just wanted to know whether there is an open-source pre-trained model that I could use for my project?
Any help would be appreciated.
Thank you.
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Hi everyone i have two datasets 24 imaged each (sampled from DIV2K dataset)
And i wan't to use one dataset to test an application for face detection( yolo face detection) and the other to test an object detection application ( yolo object detection)
And i am wondering if there is a solution to label these images before using them.
Thank you all
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Dear all,
Most of the research articles are compared and tableted the different methods and metric values when come for result & discussion. Are they really taking the result of each method before the mention in article.
Im looking for doing work on Multi scale -CNN model for Traffic Sign detection and recognition. I expecting your valuable suggestion and input for my further movement.
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Thank You Sir
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I am looking at training the Scaled YOLOv4 on TensorFlow 2.x, as can be found at this link ( https://github.com/wangermeng2021/Scaled-YOLOv4-tensorflow2).
I plan to collect the imagery, annotate the objects within the image in VOC format, and then use these images/annotations to train the large-scale model. If you look at the multi-scale training commands, they are as follows:
```
python train.py --use-pretrain True --model-type p5 --dataset-type voc --dataset dataset/pothole_voc --num-classes 1 --class-names pothole.names --voc-train-set dataset_1,train --voc-val-set dataset_1,val --epochs 200 --batch-size 4 --multi-scale 320,352,384,416,448,480,512 --augment ssd_random_crop
```
As we know that Scaled YOLOv4 (and any YOLO algorithm) likes image dimensions divisible by 32, I have plans to use larger images of 1024x1024. Is it possible to modify the ```--multi-scale``` commands to include larger dimensions such as 1024, and have the algorithm run successfully?
Here is what it would look like when modified:
```
--multi-scale 320,352,384,416,448,480,512,544,576,608,640,672,704,736,768,800,832,864,896,928,960,992,1024
```
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Thank you
J. Rafiee
! I appreciate the feedback and will be trying this later in the season.
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  • In the recent CV field, the world's top journals and conferences, we can see that many papers use multimodal / multi view information for 3D object detection.
  • However, it is rare to use multimodal information for 2D object detection in autonomous driving scene. The most recent one is' seeing through fog without seeing fog. Bijelic et al., 2019 ', but this article is mainly about the contribution to adverse weather data set.
  • How to improve the performance of 2D target detector through multimodal fusion in the automatic driving scene?
  • Or, how to use depth information for 2D target detection?
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Dear Jiawei Ma
You may use one of my article for the same issues
Satya Prakash Yadav, Vision-Based Detection, Tracking and Classification of Vehicles, IEIE Transactions on Smart Processing and Computing, SCOPUS, ISSN: 2287-5255, 9(6), pp.427-434 https://doi.org/10.5573/IEIESPC.2020.9.6.427.
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What is the difference between Easy positives/negatives and Hard positives/negatives? In object detection specifically considering focal loss
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You need to have a model, and then there can be hard positives and negatives (prediction far from GT, or small confidence) and easy ones - correct prediction, decent confidence.
Focal loss boosts loss for hard negatives: instead of improving confidence of what is already done well, the training "focuses" on hard samples.
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I'll need to research on this but I am a beginner and need to learn from the start. So, I hope someone can give me like a starting point. I've searched a lot, and there are a lot of ways of doing this, but on what way it can still be improved? I hope someone can help me on this.
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You can first scan the model and extract the cloud and its points, and then do the detection with the help of this cloud and its points.
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I am doing research work in robotics field which I have recently started. If you know any free to use robotic arm simulation software which could help me simulate and design an environment for pick and place application. Also I want to use pixy cam which helps in object detection. Software should include using pixy cam 2, universal robotic arm UR5 and their integration. Thanks
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Matlab
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I am trying to find a dataset which meets any of the following requirements.
1. Dataset of RGB or RGBD images with object bbox annotations, 6d pose annotations and grasp annotations.
2. Dataset of RGB or RGBD images with object bbox annotations and grasp annotations.
I have worked with Cornell grasp dataset and linemod 6d object pose estimation dataset separately. I am trying to build a unified model that does all these tasks together and trying to find available dataset for the same.
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Raoul G. C. Schönhof Thanks for the response. I had looked into linemod, occlusion, YCB when I was trying pose estimation separately.
My main focus is robotic grasp detection. I am trying to design a unified model that does grasp detection + object detection + pose estimation
or
grasp detection + object detection.
Thanks
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Hello everyone,
I need to annotate the MRI images (with .nii.gz format). Each image includes several slices. What I need is an annotation tool that can annotate the area of interest and propagate it in all slices? (considering that the location of object changes in different slices).
Thank you all in advance.
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I would recommend looking at these two tools:
  • ITK-SNAP (http://www.itksnap.org): This is a relatively simple tool to use for segmenting images.
  • 3D Slicer (https://www.slicer.org/): This is a very versatile tool with an active community and a sizeable selection of plug-ins.
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Hello, I am trying to understand various approaches to 3DOD (3D Object Detection) and trying to figure out the most tested one.
I aim to develop a detector for my rover to aid it in detecting pose of a custom object in outdoors (a table for instance). The object is not something already in the KITTI dataset, so requires training from scratch. As I do not have access to a 3d LiDAR, I can have stereo or monocular camera.
I have come across various implementations like [RTM3D](https://github.com/maudzung/RTM3D) and other [methods that use geometry and deep learning](https://github.com/skhadem/3D-BoundingBox).
None of these methods explain how to go forward with training a new detector for a custom object. One can observe that they need to create a data structure similar to the KITTI. The rest is trial and error. I am looking for methods which have been validated and save my time. Any help is appreciated. Thanks :)
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I agree with the before answers and as an additional waypoint can suggest this paper
Deleted research item The research item mentioned here has been deleted
Of course, this preprint considered a Lidar system but the common methodology for Deep Learning can be used as well.
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I read more paper and wrote for example: "real-time detection of people from the RGB-D image"
or "robust detection and identification of a person in real-time (around 0.3 s)"
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We are doing a project in which we are detecting (using YOLOv4) runway debris using a fixed camera mounted at a pole on the side of the runway. We want to find out the position of the object with respect to the runway surface. Does anyone know about any algorithm or technique that will help us find out the position of the object with respect to runway dimensions?
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Locate object in imageObject
Step 1: Read Images. Read the reference image containing the object of interest. Step 2: Detect Feature Points. Detect feature points in both images. ...
Step 3: Extract Feature Descriptors. ...
Step 4: Find Putative Point Matches. ...
Step 5: Locate the Object in the Scene Using Putative Matches. ...
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I am developing a machine-learning model to make accurate and fast counting of metal pipe with difference cross-sectional shapes. Well-define rectangular, triangular and circular shapes are quite ok to do, but the C shape metal is really complicated especially when they overlap one another as shown in the attached photo. Anyone has any suggestion of a model that can count overlapping object? Thanks in advance.
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I was thinking about the small object detection problem, and while surfing found a reasonable amount of paper those are working on Faster RCNN to solve this issue. Why they choose Faster RCNN instead of other state-of-the-art methods? I want to know the technical reasons. Thanks in advance.
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Mehnaz Tabassum Welcome, I will inform you if I get the answer to your question.
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I implemented an image classifier and object detection model. I added a new class every day to my model and the data set growing too. I wanted to ask if anyone has the same experience? at now it working fine. Any suggestions about further problem?
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What can be the different parameters of comparison and Can it be used for face recognition or speech recognition as well.I agree there are papers published but I have not managed to find genuine parameters to justify Optical flow method for object or moving object detection
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Thanks Aparna Mam Kindly elaborate what could be the parameters for Accuracy and explain feature sets you will consider
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I have 6600 images and I am supposed to know the rotation of the object in each image. So, given an image, I want to regress to a single value.
My attempt: I use Resnet-18 to extract a feature vector of length 1000 from an image. This is then passed to three fully-connected layers: fc(1000, 512) -> fc(512, 64) -> fc(64, 1)
The problem I am facing right now is that my training loss and validation loss immediately go down after the first 5 epochs and then they barely change. But my training and validation accuracy fluctuates wildly throughout.
I understand that I am experiencing over-fitting and I have done the following to deal with it:
(1) data augmentation (Gaussian noise and color jittering)
(2) L1 regularization
(3) dropout
So far, nothing seems to be changing the results much. The next thing I haven't tried is reducing the size of my neural net. Will that help? If so, how should I reduce the size?
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I guess I should have clarified my question a bit more. The images are taken from the perspective of a car (the KITTI dataset) and I want to know their rotation from that perspective. Here's an example: https://www.youtube.com/watch?v=kvYBboMaIbs
So, I want to find the orientation of the 3D bounding box (not all of the 3D bounding box coordinates, just the azimuth angle).
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I have tried changing parameter hue to higher values and hence obtained a higher mAp ,but in latter cases i think overfitting became a problem and the highest mAp model detection was less accurate.Please share ideas if you are working on a similar design .
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you can increase dateset numbers
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For my Ph.D., suggest some research areas/ problem statement in object detection.
Thanks in advance.
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Dear Sriram
1) TRACKING OBJECTS
2) OCR
3) Self Driving Cars
4) FACE DETECTION AND FACE RECOGNITION
5) IDENTITY VERIFICATION THROUGH IRIS CODE
6) OBJECT EXTRACTION FROM AN IMAGE OR VIDEO
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Hi, I am doing object detection. To improve my detection result I want to do Hard negative mining to minimize false detection. But I don't know that how to do it? Can someone explain it with providing MATLAB/c/c++ code.
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Refer this link to get more details about hard negative mining: http://proceedings.mlr.press/v39/canevet14a.pdf
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In the research paper, Histograms of Oriented Gradients for Human Detection, the images used are of 64X128 pixels. Do we need to crop our images dataset for applying histogram of oriented gradient?
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I agree with Vadym. I have used the same size of the dataset with FFT.
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Is it possible to determine any Physics Law from a moving object using deep learning model algorithm or can I train my model to detect it ??
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I have tried tabula-py library and java tool so far but it results in many false positives ( i.e. telling that a table is present when not the case).
Some of the cases were
content 1 content 3
content 2 content 4
If text is written in the above manner, then also it marks it as tabular data. Is there any solution that does the task better and handles the above problem. ( including Deep learning or other techniques).
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The Excalibur, which is built on top of camelot:
Best Software to Extract Tables from PDF (and export them to Excel, CSV, …)
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Hi,
as deep learning is a data-driven approach, the crucial is to have quality data. There exist a lot of datasets for free, but they differ in the quality of labels.
I'm now working on an index, which can tell a researcher quality of the labels, so the researcher may decide if such a dataset is useful nor not. I do have established a pipeline on how to produce such an index in a fully autonomous way. Note, I'm focusing on object detection tasks only, i.e., labels given as bounding-boxes.
The question is: does such the index exist already? I googled a lot and find nothing. It would be nice to compare our approach with existing ones.
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I am trying to implement object detection using C++. I have hyperspectral data in the form of .bil file along with header file. Can anyone help me in accessing .bil files using C++?
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As I know GDAL library cab handle this data. follow below link:
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In the case of object detection or abnormal cell or tissue e.g., tumor, detection segmentation is an important part. Sometimes FCM and K-means clustering is used for segmentation. I want to know which method is best for segmentation in MRI or other radio imaging for the detection of abnormalities.
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Hi;
You can read it in the following link:
Technical Report A REVIEW ON MRI IMAGE SEGMENTATION TECHNIQUES
Regards
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Hall scan data can consist of point clouds, (3D) images and videos...
I'm curious if you know about any projects or ongoing research in this area.
The aim of the object detection could be the creation of a map or a CAD model.
Thanks in advance! :)
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I suggest you to use the OpenCv library is very powerful in detecting the object whit new algorithms and methods. I was use in my article in sector automotive , we can see you my article in profile , I applied in real-time.
I hope that be Clair for you. @Carina Mieth
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We are working on an Automated Foreign Object Detection on Runway (FYP Project). It is a system to detect the FODs (Foreign Object Debris) on the surface of Runways. It also detects other anomalies like wildlife, snow, ice pavement, cracks, etc. in all weather conditions (like fog/smog, rain, dark weather etc.) Cameras will be mounted on poles at the sides of Runways to detect them and report to the Airport staff in Real-Time.
I am not sure what technique will be best for its implementation as I am new to this field. I am currently researching about Keras, YOLO, DNN, R-CNN and others. I want your opinion on how should we implement it,
Thank you.
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Before considering which ML implementation to choose I would suggest that you consider how are you going to construct the feature vector and what kind of preprocessing are you going to use. Each of the objectives above present different challenges and different degrees of difficulty (identification under different lighting, visibility, movement vs stationary objects, etc.) and I would highly recommend treating them separately on different image processing/ML systems
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Hello,
I am searching for literature on methods employed for choosing object classification when multiple object classifications (e.g. labels) are given with relatively high-confidence during analysis of a live video feed frame by frame (e.g. frame0:truck, frame1:sedan, frame2:sedan, frame3:truck)?
For instance, say a large truck enters the field-of-view of the video camera and is detected. The image segment within the provided bounding box from the detection algorithm is classified as a sedan with 95% confidence in the first frame, then subsequently classified as a truck with 95% confidence in the following frame. This process continues, resulting in multiple object classifications (i.e. labels) that vary between sedan and truck with high confidence values. How can I select the correct optimal classification? Are there methods that utilize all the classification (labels) of the object to select the optimal classification?
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Hi Ian Robert Stewart,
I Would suggest you use a non-maximum suppression technique iterating over bounding boxes from the frame over a short time period. consider the first 5 frames from your example when you run non-maximum suppression create a custom sorting metric to use the number of occurrence and score to pick out the best class and bounding box and keep moving to the next 5 frames. (reference for non-maximum suppression https://www.pyimagesearch.com/2015/02/16/faster-non-maximum-suppression-python/). If you are facing the same issue with moving objects it's gonna be difficult to do this.
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i am trying to do preprocessing step in my object detection that is edge detection. but i wonder how to do it. whether i have to have dataset that converted to edge first or no?
in my understanding, i should do the step like this:
1. input image
2. do grayscaling
3. do filtering
4. do edge
5. output of process 4, will be input in object detection method. the edge of object will be extracted to get the feature.
6. data training from dataset inria (without convert it to edge) will be trained with svm classifier.
7. do the matching using the object detection
is that correct?
Please kindly help. Thank you
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Add a step 2.5 of thresholding to put it in black and white.
Most pictures are very noisy so in step 3 I would start with erosion and dilation steps to clean up the image a bit and then add other filtering. further depending on the type of noise involved it may be necessary to apply other filtering techniques including some that may be on the frequency domain(it all depends on the type of noise, periodicity or randomness, etc.).
Depending on your dataset another thing that may be necessary is appropriate scaling of the image (do after filtering ).
As a final note, depending on what you are trying to identify and the categories, I would try thinning and chain codes as a preliminary pre-processing stage to pass it on to the SVM classifier
If you can point me to the specific link to the dataset i could be of more assistance
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i can load efficientnet features with centernet like this :
from efficientnet_pytorch import EfficientNet
base_model = EfficientNet.from_pretrained('efficientnet-b1')
x_center = x[:, :, :, IMG_WIDTH // 8: -IMG_WIDTH // 8]
feats = base_model.extract_features(x_center)
but in Deep Layer Aggregation(DLA34) extract_features() function is not available,i am new to object detection,how can i extract_features from dla34 and other networks like densenet with centernet?
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class CentDla(nn.Module):
'''Mixture of previous classes'''
def __init__(self, n_classes):
super(CentDla, self).__init__()
self.base_model = dla34(pretrained=True)
# Lateral layers convert resnet outputs to a common feature size
self.lat8 = nn.Conv2d(128, 256, 1)
self.lat16 = nn.Conv2d(256, 256, 1)
self.lat32 = nn.Conv2d(512, 256, 1)
self.bn8 = nn.BatchNorm2d(256)
self.bn16 = nn.BatchNorm2d(256)
self.bn32 = nn.BatchNorm2d(256)
self.conv0 = double_conv(5, 64)
self.conv1 = double_conv(64, 128)
self.conv2 = double_conv(128, 512)
self.conv3 = double_conv(512, 1024)
self.mp = nn.MaxPool2d(2)
self.up1 = up(1282 , 512) #+ 1024
self.up2 = up(512 + 512, 256)
self.outc = nn.Conv2d(256, n_classes, 1)
def forward(self, x):
batch_size = x.shape[0]
mesh1 = get_mesh(batch_size, x.shape[2], x.shape[3])
x0 = torch.cat([x, mesh1], 1)
x1 = self.mp(self.conv0(x0))
x2 = self.mp(self.conv1(x1))
x3 = self.mp(self.conv2(x2))
x4 = self.mp(self.conv3(x3))
#feats = self.base_model.extract_features(x)
# Run frontend network
feats32 = self.base_model(x)[5]
#lat8 = F.relu(self.bn8(self.lat8(feats8)))
#lat16 = F.relu(self.bn16(self.lat16(feats16)))
lat32 = F.relu(self.bn32(self.lat32(feats32)))
# Add positional info
mesh2 = get_mesh(batch_size, lat32.shape[2], lat32.shape[3])
feats = torch.cat([lat32, mesh2], 1)
#print(feats.shape)
#print (x4.shape)
x = self.up1(feats, x4)
x = self.up2(x, x3)
x = self.outc(x)
return x
# Gets the GPU if there is one, otherwise the cpu
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
n_epochs = 20 #6
n_classes = 8
model = CentDla(n_classes).to(device)
optimizer = optim.AdamW(model.parameters(), lr=0.001)
#optimizer = RAdam(model.parameters(), lr = 0.001)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=max(n_epochs, 10) * len(train_loader) // 3, gamma=0.1)
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for my object detection model yolo i have annotation xml file with the bounding box coordinates (ymin, xmin, ymax, xmax) but don't have height and width information how can i calculate them or if there is a python script to extract them
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A good tool for image analysis is Matlab. You can use the size() function: [the Length, width] = size(binary Image); The first return argument, the Length, is the number of rows (the height), and the second output argument, width, is the number of columns .
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Hi everyone,
I'm working on a task which consists in detecting if there are some foreign objects (like bolts, pliers etc.) inside the interior of an aircraft's wing. Problem is that my neural networks should find out-of-place objects inside the specified environment. I was thinking to train pytorch pre-trained CNN like Resnet101 or Inceptionv3. Do you know any further suitable CNN for this particular use-case or a different technique? I am open to any kind of suggestion. Thanks in advance.
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First of all, if you need high fidelity in your predictions I recommend creating an own data set for your use case and train the network on these data. Transfer learning with weights from ImageNet might improve your accuracy and speed up the training progress.
For the network, I recommend a simple and fast network like RetinaNet ( ) which can be used with different backbones, such as VGG, ResNet etc.
You will find a good implementation with Keras here:
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There are many object detection methods out there. The newest object detection method is object detection with neural network. But there are also traditional methods, on of the traditional method is HOG method.
I want to know is HOG still relevant to be used in object detection? what is the advantage of using HOG method instead neural network method?
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Traditional methods can be used as a benchmark against which newer methods can be tested for improved performance.
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I have been trying to tackle a problem where I need to track multiple people through multiple camera viewpoints on a real-time basis.
I found a solution DeepCC (https://github.com/daiwc/DeepCC) on DukeMTMC dataset but unfortunately, this solution has been taken down because of data confidentiality issues. They were using Fast R-CNN for object detection, triplet loss for Re-identification and DeepSort for real-time multiple object tracking.
Can someone share some other resources regarding the same problem?
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Thank you Aparna Sathya Murthy for sharing the sources. It seems like the above 2 have their software version.
I am looking more like an open-source code like on Github that provides me with basic multi camera multi object tracking functionality and then, I can built on top of that. Appreciate your response :)
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Small object detection is always challenging due to the limitations of available information. Is it a good idea to use GAN to improve the feature presentation for small objects? What can we do ?
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What are the characteristics of small objects and how to design an algorithm based on the characteristics ? To my knowledge, feature fusion and context learning are usually used to improve object detection. However, it is hard to why they improve the detection results. Are there some algorithms designed just for small object detection ?
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you could refer to this paper
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I want to Develop an Object Detection RESTful web service that will take images as payload and return the coordinates of the bounding boxes or the image itself marked with the detected objects.
Note: Tensorflow Object Detection API makes it easy to detect objects by using pre-trained object detection models.
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I am thinking of utilizing YOLO algorithm and Faster RCNN for localization and detection task, is this easy?
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well, you are trying to maintain the higher accuracy and reduce complexity. Faster RCNN is using VGG as a backbone model and is a two-stage object detector i.e. RPN and VGG while YOLO(Darknet backbone) is a single shot detector utilizing anchor boxes. one possible combination is to try using Darknet in faster RCNN replacing VGG and vice versa for YOLO. the other possible option is to remove RPN and utilize anchor boxes similar to YOLO in faster RCNN.
But again, it depends on your problem. what kind of dataset you want to use. like YOLO results are extremely bad on the self-driving cars dataset.
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We are working on a project to detect foreign objects on the surface of a runway. We want to know which camera will be best for this purpose. The runway is 46m wide and it should be able to detect very small objects on it. The angle of the camera should be high for maximum area coverage. It should be able to work in all weather conditions like rain, fog, low light etc. Can you recommend us some cameras that will be good for this project? We will use Artificial Intelligence techniques to detect the object.
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It is always preferable to choose types of cameras with high accuracy in addition to ensuring that it can deal with the fluctuating climate conditions as it will be in an external environment in this location.
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I am working on vehicle detection task on UA-DETRAC dataset. I am getting a precision recall curve (PR curve) as shown in figure.
Is it acceptable ? Because mostly PR-curve remains high at start and then decreases suddenly. I checked my code and I believe it is correct. But in the literature PR-curve for this dataset looks as shown in attached figure. Do you the curve I got is also correct ??
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Ok. Thank you very much.
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In general the first principle is a basic assumption that cannot be deduced any further. Related to different fields of human activity there are different definitions of first principles, for example for engineering those are the laws of physics. Often great innovation in science/engineering happens when the new idea is not build on top of the current state of the art or commonly accepted technology. Instead the problem is initiated from those first principles or in other words "what we know for sure" and re-build from there.
So, what are the first principles known so far in computer vision, particularly in object detection. Are there fundamental "can do" and "cant do" that take its roots and proofs in computer science, physics, mathematics?
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Object recognition is a computer vision technique for identifying objects in images or videos. ... Using object recognition to identify different categories of objects. Object recognition is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost.
Size, color, and shape are some commonly used features. system depend on the types of objects to be recognized and the organization of the model database. Using the detected features in the image, the hypothesizer assigns likelihoods to objects present in the scene.
Refer the following link:
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I am using Mask-RCNN model with ResNet50 backbone for nodule detection in ultrasound images. My dataset consists of 500 US images. Maximum object detection accuracy for training set is approximately 54% (using data augmentation and hyper-parameter tuning). The accuracy of object detection on my test set is even lower. Are there any suggestions for improving object detection accuracy?
Many thanks in advance,
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U-Net architecture works much better for BUS segmentation. Your dataset contains enough data and In most of the cases the augmentation will not help because the BUS images contain speckle noise, artifacts, and ...
You must use different BUS dataset to make your model robust. Some other public BUS dataset:
Also, there is a collection of breast ultrasound images here
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I am particularly looking for dataset containing cameras capturing videos in a distributed setup. The videos, captured by multiple cameras have to be correlated. Is there any benchmark dataset on that? I found some of the existing datasets for distributed networks, but the factor of correlation is absent. It would be great to get some help.
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Hi there!
I would like to count and categorize embryos on dozens of pictures. It would be quite time consuming to do that manually so I was thinking about a Python solution to automatize that process.
A sample picture is attached. This picture has all the challenges and nearly all the developmental stages (2 cell; 4-8 cell; morula) that I am facing of.
Does any of you have a good recommendation on that (libraries; Git; articles etc)?
Thanks in advance!
Bálint
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Ok; I will give it a try!
Thank you very much!
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Hi Guys
I am experiencing that when i am using a R-CNN detector for object detection , when i increase the training data , i have bad classification and overfitting
Is there some one experienced this issue and the reason for that?
Best
Abdussalam
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Use a validation/training set, increase it step by step and plot it. The Bias and Variance will show if you have an overfit. Another test is Cross validation.
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I'm looking for a point cloud processing library that includes classification / object detection and tracking. After some web surfing i got an impression that the Point Cloud Library (PCL) written in C/C++ is the only option with ALL abovementioned capabilities. There is no problem to start the development in C/C++. However, it would be better to use python instead for faster prototyping and building the first working version for further experimenting. The performance / real-time processing are not pursued at first. Did anyone meet similar problem and what was your library of choice? Thanks
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While PCL is nice framework it is only used to a certain extent. Once you start developing your own methods you might find it a bit limiting. It also does not contain all the latest state-of-the-art methods, which is why it is sometimes easier to just develop your own point-cloud processing pipeline. Of course, this depends on the task. But it is simple to just take a data reading library for the format of your desire and do the rest on your own.
What kind of goal/application do you have in mind?