Performance Analysis of the YOLOv4 Algorithm for Pavement Damage Image Detection with Different Embedding Positions of CBAM Modules
One of the most critical tasks for pavement maintenance and road safety is the rapid and correct identification and classification of asphalt pavement damages. Nowadays, deep learning networks have become the popular method for detecting pavement cracks, and there is always a need to further improve...
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/10180 |
| _version_ | 1797480391921106944 |
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| author | Li Li Baihao Fang Jie Zhu |
| author_facet | Li Li Baihao Fang Jie Zhu |
| author_sort | Li Li |
| collection | DOAJ |
| description | One of the most critical tasks for pavement maintenance and road safety is the rapid and correct identification and classification of asphalt pavement damages. Nowadays, deep learning networks have become the popular method for detecting pavement cracks, and there is always a need to further improve the accuracy and precision of pavement damage recognition. An improved YOLOv4-based pavement damage detection model was proposed in this study to address the above problems. The model improves the saliency of pavement damage by introducing the convolutional block attention module (CBAM) to suppress background noise and explores the influence of the embedding position of the CBAM module in the YOLOv4 model on the detection accuracy. The K-means++ algorithm was used to optimize the anchor box parameters to improve the target detection accuracy and form a high-performance pavement crack detection model called YOLOv4-3. The training and test sets were constructed using the same image data sources, and the results showed the mAP (mean average precision) of the improved YOLOv4-3 network was 2.96% higher than that before the improvement. The experiments indicate that embedding CBAM into the Neck module and the Head module can effectively improve the detection accuracy of the YOLOv4 model. |
| first_indexed | 2024-03-09T21:59:19Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T21:59:19Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1e90158ac66743579d5cf53c715b26042023-11-23T19:52:21ZengMDPI AGApplied Sciences2076-34172022-10-0112191018010.3390/app121910180Performance Analysis of the YOLOv4 Algorithm for Pavement Damage Image Detection with Different Embedding Positions of CBAM ModulesLi Li0Baihao Fang1Jie Zhu2School of Mechanics and Engineering Science, Shanghai University, Shanghai 200444, ChinaSchool of Mechanics and Engineering Science, Shanghai University, Shanghai 200444, ChinaCATS Testing Technology (Beijing) Co., Ltd., Beijing 100029, ChinaOne of the most critical tasks for pavement maintenance and road safety is the rapid and correct identification and classification of asphalt pavement damages. Nowadays, deep learning networks have become the popular method for detecting pavement cracks, and there is always a need to further improve the accuracy and precision of pavement damage recognition. An improved YOLOv4-based pavement damage detection model was proposed in this study to address the above problems. The model improves the saliency of pavement damage by introducing the convolutional block attention module (CBAM) to suppress background noise and explores the influence of the embedding position of the CBAM module in the YOLOv4 model on the detection accuracy. The K-means++ algorithm was used to optimize the anchor box parameters to improve the target detection accuracy and form a high-performance pavement crack detection model called YOLOv4-3. The training and test sets were constructed using the same image data sources, and the results showed the mAP (mean average precision) of the improved YOLOv4-3 network was 2.96% higher than that before the improvement. The experiments indicate that embedding CBAM into the Neck module and the Head module can effectively improve the detection accuracy of the YOLOv4 model.https://www.mdpi.com/2076-3417/12/19/10180pavement maintenanceYOLOv4crack identificationCBAM |
| spellingShingle | Li Li Baihao Fang Jie Zhu Performance Analysis of the YOLOv4 Algorithm for Pavement Damage Image Detection with Different Embedding Positions of CBAM Modules Applied Sciences pavement maintenance YOLOv4 crack identification CBAM |
| title | Performance Analysis of the YOLOv4 Algorithm for Pavement Damage Image Detection with Different Embedding Positions of CBAM Modules |
| title_full | Performance Analysis of the YOLOv4 Algorithm for Pavement Damage Image Detection with Different Embedding Positions of CBAM Modules |
| title_fullStr | Performance Analysis of the YOLOv4 Algorithm for Pavement Damage Image Detection with Different Embedding Positions of CBAM Modules |
| title_full_unstemmed | Performance Analysis of the YOLOv4 Algorithm for Pavement Damage Image Detection with Different Embedding Positions of CBAM Modules |
| title_short | Performance Analysis of the YOLOv4 Algorithm for Pavement Damage Image Detection with Different Embedding Positions of CBAM Modules |
| title_sort | performance analysis of the yolov4 algorithm for pavement damage image detection with different embedding positions of cbam modules |
| topic | pavement maintenance YOLOv4 crack identification CBAM |
| url | https://www.mdpi.com/2076-3417/12/19/10180 |
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