Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments
Coastal areas face severe corrosion issues, posing significant risks and economic losses to equipment, personnel, and the environment. YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion...
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MDPI AG
2024-10-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/10/1754 |
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| author | Qifeng Yu Yudong Han Xinjia Gao Wuguang Lin Yi Han |
| author_facet | Qifeng Yu Yudong Han Xinjia Gao Wuguang Lin Yi Han |
| author_sort | Qifeng Yu |
| collection | DOAJ |
| description | Coastal areas face severe corrosion issues, posing significant risks and economic losses to equipment, personnel, and the environment. YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion images often feature complex characteristics and high variability in detection targets, presenting significant challenges for YOLO v5 in recognizing and extracting corrosion features. To improve the detection performance of YOLO v5 for corrosion image features, this study investigates two enhanced models: EfficientViT-NWD-YOLO v5 and Gold-NWD-YOLO v5. These models specifically target improvements to the backbone and neck structures of YOLO v5, respectively. The performance of these models for corrosion detection is analyzed in comparison with both YOLO v5 and NWD-YOLO v5. The evaluation metrics including precision, recall, F1-score, Frames Per Second (FPS), pre-processing time, inference time, non-maximum suppression time (NMS), and confusion matrix were used to evaluate the detection performance. The results indicate that the Gold-NWD-YOLO v5 model shows significant improvements in precision, recall, F1-score, and accurate prediction probability. However, it also increases inference time and NMS time, and decreases FPS. This suggests that while the modified neck structure significantly enhances detection performance in corrosion images, it also increases computational overhead. On the other hand, the EfficientViT-NWD-YOLO v5 model shows slight improvements in precision, recall, F1-score, and accurate prediction probability. Notably, it significantly reduces inference and NMS time, and greatly improves FPS. This indicates that modifications to the backbone structure do not notably enhance corrosion detection performance but significantly improve detection speed. From the application perspective, YOLO v5 and NWD-YOLO v5 are suitable for routine corrosion detection applications. Gold-NWD-YOLO v5 is better suited for scenarios requiring high precision in corrosion detection, while EfficientViT-NWD-YOLO v5 is ideal for applications needing a balance between speed and accuracy. The findings can guide decision making for corrosion health monitoring for critical infrastructure in coastal areas. |
| format | Article |
| id | doaj-art-e3a1e50eb7f144a3a56c023a030b4657 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-e3a1e50eb7f144a3a56c023a030b46572025-08-20T02:11:00ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-10-011210175410.3390/jmse12101754Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal EnvironmentsQifeng Yu0Yudong Han1Xinjia Gao2Wuguang Lin3Yi Han4College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Transport and Communications, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Transport and Communications, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Transport and Communications, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Transport and Communications, Shanghai Maritime University, Shanghai 201306, ChinaCoastal areas face severe corrosion issues, posing significant risks and economic losses to equipment, personnel, and the environment. YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion images often feature complex characteristics and high variability in detection targets, presenting significant challenges for YOLO v5 in recognizing and extracting corrosion features. To improve the detection performance of YOLO v5 for corrosion image features, this study investigates two enhanced models: EfficientViT-NWD-YOLO v5 and Gold-NWD-YOLO v5. These models specifically target improvements to the backbone and neck structures of YOLO v5, respectively. The performance of these models for corrosion detection is analyzed in comparison with both YOLO v5 and NWD-YOLO v5. The evaluation metrics including precision, recall, F1-score, Frames Per Second (FPS), pre-processing time, inference time, non-maximum suppression time (NMS), and confusion matrix were used to evaluate the detection performance. The results indicate that the Gold-NWD-YOLO v5 model shows significant improvements in precision, recall, F1-score, and accurate prediction probability. However, it also increases inference time and NMS time, and decreases FPS. This suggests that while the modified neck structure significantly enhances detection performance in corrosion images, it also increases computational overhead. On the other hand, the EfficientViT-NWD-YOLO v5 model shows slight improvements in precision, recall, F1-score, and accurate prediction probability. Notably, it significantly reduces inference and NMS time, and greatly improves FPS. This indicates that modifications to the backbone structure do not notably enhance corrosion detection performance but significantly improve detection speed. From the application perspective, YOLO v5 and NWD-YOLO v5 are suitable for routine corrosion detection applications. Gold-NWD-YOLO v5 is better suited for scenarios requiring high precision in corrosion detection, while EfficientViT-NWD-YOLO v5 is ideal for applications needing a balance between speed and accuracy. The findings can guide decision making for corrosion health monitoring for critical infrastructure in coastal areas.https://www.mdpi.com/2077-1312/12/10/1754marine corrosion detectionYOLO v5model improvementdetection performance metricscomparative analysis |
| spellingShingle | Qifeng Yu Yudong Han Xinjia Gao Wuguang Lin Yi Han Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments Journal of Marine Science and Engineering marine corrosion detection YOLO v5 model improvement detection performance metrics comparative analysis |
| title | Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments |
| title_full | Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments |
| title_fullStr | Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments |
| title_full_unstemmed | Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments |
| title_short | Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments |
| title_sort | comparative analysis of improved yolo v5 models for corrosion detection in coastal environments |
| topic | marine corrosion detection YOLO v5 model improvement detection performance metrics comparative analysis |
| url | https://www.mdpi.com/2077-1312/12/10/1754 |
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