Improved YOLOv3-Based Bridge Surface Defect Detection by Combining High- and Low-Resolution Feature Images
Automatic bridge surface defect detection is of wide concern; it can save human resources and improve work efficiency. The object detection algorithm, especially the You Only Look Once (YOLO) series of networks, has important potential in real-time object detection because of its fast detection spee...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/12/8/1225 |
| _version_ | 1797411019006410752 |
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| author | Shuai Teng Zongchao Liu Xiaoda Li |
| author_facet | Shuai Teng Zongchao Liu Xiaoda Li |
| author_sort | Shuai Teng |
| collection | DOAJ |
| description | Automatic bridge surface defect detection is of wide concern; it can save human resources and improve work efficiency. The object detection algorithm, especially the You Only Look Once (YOLO) series of networks, has important potential in real-time object detection because of its fast detection speed, and it provides an efficient and automatic detection method for bridge surface defect detection. Hence, this paper employs an improved YOLOv3 network for detecting bridge surface defects (cracks and exposed rebar) and compares the effects of the advanced YOLOv2, YOLOv3 and faster region-based convolutional neural network (Faster RCNN) in bridge surface defect detection, and then two approaches (transfer learning and data augmentation) are used to improve the YOLOv3. The results confirm that, by combining high- and low-resolution feature images, the YOLOv3 improves the detection effect of the YOLOv2 (using single-resolution feature images); the average precision (AP) value of the improved YOLOv3 (0.9–0.91) is 6–10% higher than that of the YOLOv2 (0.83–0.86). Then, the anti-noise abilities of the YOLOv2 and YOLOv3 are studied by introducing white Gaussian noise, and the YOLOv3 is better than the YOLOv2. Simultaneously, the YOLO series of detectors perform better in detection speed; the detection speed of the improved YOLOv3 (FPS (frames per second) = 23.8) is 103 times that of the Faster RCNN (FPS = 0.23) with comparable mAP values (improved YOLOv3 = 0.91; Faster RCNN = 0.9). It is demonstrated that, in consideration of detection precision and speed, the proposed improved YOLOv3 is a decent detector for fast and real-time bridge defect detection. |
| first_indexed | 2024-03-09T04:38:55Z |
| format | Article |
| id | doaj.art-43cd8852618d4bc4b18c872f8225f6ef |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T04:38:55Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-43cd8852618d4bc4b18c872f8225f6ef2023-12-03T13:24:11ZengMDPI AGBuildings2075-53092022-08-01128122510.3390/buildings12081225Improved YOLOv3-Based Bridge Surface Defect Detection by Combining High- and Low-Resolution Feature ImagesShuai Teng0Zongchao Liu1Xiaoda Li2School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Civil Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Civil Engineering, Guangzhou University, Guangzhou 510006, ChinaAutomatic bridge surface defect detection is of wide concern; it can save human resources and improve work efficiency. The object detection algorithm, especially the You Only Look Once (YOLO) series of networks, has important potential in real-time object detection because of its fast detection speed, and it provides an efficient and automatic detection method for bridge surface defect detection. Hence, this paper employs an improved YOLOv3 network for detecting bridge surface defects (cracks and exposed rebar) and compares the effects of the advanced YOLOv2, YOLOv3 and faster region-based convolutional neural network (Faster RCNN) in bridge surface defect detection, and then two approaches (transfer learning and data augmentation) are used to improve the YOLOv3. The results confirm that, by combining high- and low-resolution feature images, the YOLOv3 improves the detection effect of the YOLOv2 (using single-resolution feature images); the average precision (AP) value of the improved YOLOv3 (0.9–0.91) is 6–10% higher than that of the YOLOv2 (0.83–0.86). Then, the anti-noise abilities of the YOLOv2 and YOLOv3 are studied by introducing white Gaussian noise, and the YOLOv3 is better than the YOLOv2. Simultaneously, the YOLO series of detectors perform better in detection speed; the detection speed of the improved YOLOv3 (FPS (frames per second) = 23.8) is 103 times that of the Faster RCNN (FPS = 0.23) with comparable mAP values (improved YOLOv3 = 0.91; Faster RCNN = 0.9). It is demonstrated that, in consideration of detection precision and speed, the proposed improved YOLOv3 is a decent detector for fast and real-time bridge defect detection.https://www.mdpi.com/2075-5309/12/8/1225YOLO networksurface defecttransfer learningdata augmentationdetection precision and speed |
| spellingShingle | Shuai Teng Zongchao Liu Xiaoda Li Improved YOLOv3-Based Bridge Surface Defect Detection by Combining High- and Low-Resolution Feature Images Buildings YOLO network surface defect transfer learning data augmentation detection precision and speed |
| title | Improved YOLOv3-Based Bridge Surface Defect Detection by Combining High- and Low-Resolution Feature Images |
| title_full | Improved YOLOv3-Based Bridge Surface Defect Detection by Combining High- and Low-Resolution Feature Images |
| title_fullStr | Improved YOLOv3-Based Bridge Surface Defect Detection by Combining High- and Low-Resolution Feature Images |
| title_full_unstemmed | Improved YOLOv3-Based Bridge Surface Defect Detection by Combining High- and Low-Resolution Feature Images |
| title_short | Improved YOLOv3-Based Bridge Surface Defect Detection by Combining High- and Low-Resolution Feature Images |
| title_sort | improved yolov3 based bridge surface defect detection by combining high and low resolution feature images |
| topic | YOLO network surface defect transfer learning data augmentation detection precision and speed |
| url | https://www.mdpi.com/2075-5309/12/8/1225 |
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