YOLO v5 Architecture Overview [3]

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Figure 1. Top-1 one-crop accuracy versus amount of operations required...

Figure 3. YOLO v5 Architecture Overview [3]

Figure 4. YOLOv5 Validation Loss Curves Negative mAP Positive mAP Total...

Figure 5. Demo Result of MTCNN + ResNet18 [21]

Real-time Face Mask Detection in Video Data

Preprint

Full-text available

May 2021

In response to the ongoing COVID-19 pandemic, we present a robust deep learning pipeline that is capable of identifying correct and incorrect mask-wearing from real-time video streams. To accomplish this goal, we devised two separate approaches and evaluated their performance and run-time efficiency. The first approach leverages a pre-trained face...

Context 1

... leads us to the fifth version, YOLO v5, developed by the company Ultralytics. This latest iteration utilizes Cross Stage Partial Network (CSPNet) [34] as the model backbone and Path Aggregation Network (PANet) [23] as the neck for feature aggregation (see Figure 3). These improvements have led to better feature extraction and a significant boost in the mean averaged precision score. ...

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Proposed architecture for anomaly detection in surveillance videos

Overall structure of BS for UCSD Ped2‐Test004 (a) Original frame (b)...

Structure of fully convolutional autoencoder

Object detection using YOLOv5 on UCSD Ped2‐Test 04

Exemplary frames for normal and abnormal scenes from different datasets

Real‐time video anomaly detection for smart surveillance

Article

Full-text available

Dec 2022

Manal Mostafa Ali

Human monitoring of surveillance cameras for anomaly detection may be a monotonous task as it requires constant attention to judge if the captured activities are anomalous or suspicious. This paper exploits background subtraction (BS), convolutional autoencoder, and object detection for a fully automated surveillance system. BS was performed by mod...

YOLO v5 Architecture Overview [3]

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