Ying Chen’s research while affiliated with Shantou University and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (6)


Segmentation of retinal vessels in fundus images based on U-Net with self-calibrated convolutions and spatial attention modules
  • Article

March 2023

·

32 Reads

·

7 Citations

Medical & Biological Engineering & Computing

·

Yu Xiong

·

·

[...]

·

Automated and accurate segmentation of retinal vessels in fundus images is an important step for screening and diagnosing various ophthalmologic diseases. However, many factors, including the variations of vessels in color, shape and size, make this task become an intricate challenge. One kind of the most popular methods for vessel segmentation is U-Net based methods. However, in the U-Net based methods, the size of the convolution kernels is generally fixed. As a result, the receptive field for an individual convolution operation is single, which is not conducive to the segmentation of retinal vessels with various thicknesses. To overcome this problem, in this paper, we employed self-calibrated convolutions to replace the traditional convolutions for the U-Net, which can make the U-Net learn discriminative representations from different receptive fields. Besides, we proposed an improved spatial attention module, instead of using traditional convolutions, to connect the encoding part and decoding part of the U-Net, which can improve the ability of the U-Net to detect thin vessels. The proposed method has been tested on Digital Retinal Images for Vessel Extraction (DRIVE) database and Child Heart and Health Study in England Database (CHASE DB1). The metrics used to evaluate the performance of the proposed method are accuracy (ACC), sensitivity (SE), specificity (SP), F1-score (F1) and the area under the receiver operating characteristic curve (AUC). The ACC, SE, SP, F1 and AUC obtained by the proposed method are 0.9680, 0.8036, 0.9840, 0.8138 and 0.9840 respectively on DRIVE database, and 0.9756, 0.8118, 0.9867, 0.8068 and 0.9888 respectively on CHASE DB1, which are better than those obtained by the traditional U-Net (the ACC, SE, SP, F1 and AUC obtained by U-Net are 0.9646, 0.7895, 0.9814, 0.7963 and 0.9791 respectively on DRIVE database, and 0.9733, 0.7817, 0.9862, 0.7870 and 0.9810 respectively on CHASE DB1). The experimental results indicate that the proposed modifications in the U-Net are effective for vessel segmentation. The structure of the proposed network The structure of the proposed network


CrackCLF: Automatic Pavement Crack Detection Based on Closed-Loop Feedback

January 2023

·

40 Reads

·

5 Citations

IEEE Transactions on Intelligent Transportation Systems

Automatic pavement crack detection is an important task to ensure the functional performances of pavements during their service life. Inspired by deep learning (DL), the encoder-decoder framework is a powerful tool for crack detection. However, these models are usually open-loop (OL) systems that tend to treat thin cracks as the background. Meanwhile, these models can not automatically correct errors in the prediction, nor can it adapt to the changes of the environment to automatically extract and detect thin cracks. To tackle this problem, we embed closed-loop feedback (CLF) into the neural network so that the model could learn to correct errors on its own, based on generative adversarial networks (GAN). The resulting model is called CrackCLF and includes the front and back ends, i.e. segmentation and adversarial network. The front end with U-shape framework is employed to generate crack maps, and the back end with a multi-scale loss function is used to correct higher-order inconsistencies between labels and crack maps (generated by the front end) to address open-loop system issues. Empirical results show that the proposed CrackCLF outperforms others methods on three public datasets. Moreover, the proposed CLF can be defined as a plug and play module, which can be embedded into different neural network models to improve their performances.



Figure 8. Results of comparison of proposed U-HDN with other method based on public database (From left to right: input image, ground truth, Canny, local threshold, FFA, MPS, structured prediction, ensemble network, and proposed U-HDN).
Types of cracks in road pavements.
Experimental results for different dilation rates on CFD database.
Experimental results for different dilation rates on AigleRN database.
Crack detection results on CFD.

+3

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture
  • Article
  • Full-text available

July 2020

·

743 Reads

·

129 Citations

Materials

Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.

Download

Automatic Crack Detection on Road Pavements Using Encoder Decoder Architecture

July 2020

·

520 Reads

Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low-level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.


Figure 1. Overview of the automated pavement crack detection and measurement system, (a) The raw image; (b) The ensemble network model; (c) The output image of ensemble network; (d) The crack segmentation image; (e) The extracted crack skeleton based on the medial-axis algorithm; (f) The extracted crack width and length.
Figure 3. The Pr, Re, and F1 value variations with different numbers of neural network models and thresholds based on the AigleRN database.
Figure 4. The Pr, Re, and F1 value variations with different numbers of neural network models and thresholds based on the CFD database.
Figure 7. The experimental results show crack segmentation and crack skeleton based on public databases CFD and AigleRN.
Figure 8. The experimental results show crack segmentation and crack skeleton based on public databases CFD and AigleRN. The numbers are in pixels.
Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

February 2020

·

449 Reads

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.

Citations (4)


... This model effectively suppressed noise propagation with strong robustness but showed limited effectiveness in detecting fine cracks and preserving crack integrity. Li et al. [26] proposed a CrackCLF network based on adversarial networks, embedding closed-loop feedback into the neural network to address the issue of the model's inability to automatically adapt to environmental changes and effectively extract and detect wave cracks. ...

Reference:

Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
CrackCLF: Automatic Pavement Crack Detection Based on Closed-Loop Feedback
  • Citing Article
  • January 2023

IEEE Transactions on Intelligent Transportation Systems

... The encoder reduces the spatial dimension, while the decoder employs deconvolutional layers to progressively increase the feature map's size until it matches the dimensions of the input image. Skip connections are incorporated to establish communication between the encoder and decoder, enabling the transmission of important features and details for more accurate segmentation results [35,36]. The original dimensions of the images in the dataset were initially 1920 × 1080 pixels. ...

Segmentation of retinal vessels in fundus images based on U-Net with self-calibrated convolutions and spatial attention modules
  • Citing Article
  • March 2023

Medical & Biological Engineering & Computing

... In this paper, KLD sampling based on MCL is applied to perform the navigation process, which is used to measure the two probability distributions. For the implementation of MCL, we refer to [10]. In the navigation process, control information and measurement information are used to obtain the robot's position, in the map by subscribing to the MCL algorithm's node based on the ROS [11]. ...

Building 3D Map Based on Monte Carlo Localization and Feature Extraction
  • Citing Conference Paper
  • October 2020

... Recent work has facilitated automated classification, localization, and quantification of structural defects from image data (Cha et al., 2017;Chen and Jahanshahi, 2017;Cha et al., 2018;Attard et al., 2019;Liu et al., 2019;Li et al., 2020Li et al., , 2024a. DL-based techniques have shown promise in detecting cracks in buildings (Perez et al., 2019;Jiang et al., 2021), bridges (Dais et al., 2021;Hallee et al., 2021;Loverdos and Sarhosis, 2022), tunnels (Liao et al., 2022a;Protopapadakis et al., 2019), and roads (Fan et al., 2020). For example, recent studies reported the successful detection of cracks with widths greater than ≤ 1 mm (Liao et al., 2022b;Mohammadi et al., 2019). ...

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Materials