A Tunnel Surface Diseases Detection Algorithm Based on YOLOv4
Timely detection and maintenance of tunnel diseases are very important for traffic safety. Aiming at the problem that tunnel environment is complex and variable and the contrast of surface image is low, which makes the traditional pattern recognition method cannot effectively detect diseases. In thi...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2021-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
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| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.05.100 |
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| author | LI Jia QIU Xinhua JI Yuwen |
| author_facet | LI Jia QIU Xinhua JI Yuwen |
| author_sort | LI Jia |
| collection | DOAJ |
| description | Timely detection and maintenance of tunnel diseases are very important for traffic safety. Aiming at the problem that tunnel environment is complex and variable and the contrast of surface image is low, which makes the traditional pattern recognition method cannot effectively detect diseases. In this paper, a tunnel surface disease detection method based on YOLOv4 is proposed. CSPDarknet-53 is used as the backbone network to extract features effectively, different scale features are integrated through SPP to classify and regress the disease area through YOLO layer, and the detection accuracy is effectively improved by using CIoU to calculate the regression loss. Experimental results show that the detection speed can reach 55fps by using this algorithm and NVIDIA GeForce 2080Ti. In the established surface image data set of high speed railway tunnel, the mAP reaches 65.1%, and the defect detection rate is 90.1%, which verifies the high efficiency of the algorithm. |
| format | Article |
| id | doaj-art-ddb2b04e1ac6452490de5754efb13acc |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-ddb2b04e1ac6452490de5754efb13acc2025-08-25T06:53:09ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272021-01-0138788382319570A Tunnel Surface Diseases Detection Algorithm Based on YOLOv4LI JiaQIU XinhuaJI YuwenTimely detection and maintenance of tunnel diseases are very important for traffic safety. Aiming at the problem that tunnel environment is complex and variable and the contrast of surface image is low, which makes the traditional pattern recognition method cannot effectively detect diseases. In this paper, a tunnel surface disease detection method based on YOLOv4 is proposed. CSPDarknet-53 is used as the backbone network to extract features effectively, different scale features are integrated through SPP to classify and regress the disease area through YOLO layer, and the detection accuracy is effectively improved by using CIoU to calculate the regression loss. Experimental results show that the detection speed can reach 55fps by using this algorithm and NVIDIA GeForce 2080Ti. In the established surface image data set of high speed railway tunnel, the mAP reaches 65.1%, and the defect detection rate is 90.1%, which verifies the high efficiency of the algorithm.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.05.100tunnel surface diseases detectionYOLOv4deep learningCIoU |
| spellingShingle | LI Jia QIU Xinhua JI Yuwen A Tunnel Surface Diseases Detection Algorithm Based on YOLOv4 Kongzhi Yu Xinxi Jishu tunnel surface diseases detection YOLOv4 deep learning CIoU |
| title | A Tunnel Surface Diseases Detection Algorithm Based on YOLOv4 |
| title_full | A Tunnel Surface Diseases Detection Algorithm Based on YOLOv4 |
| title_fullStr | A Tunnel Surface Diseases Detection Algorithm Based on YOLOv4 |
| title_full_unstemmed | A Tunnel Surface Diseases Detection Algorithm Based on YOLOv4 |
| title_short | A Tunnel Surface Diseases Detection Algorithm Based on YOLOv4 |
| title_sort | tunnel surface diseases detection algorithm based on yolov4 |
| topic | tunnel surface diseases detection YOLOv4 deep learning CIoU |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.05.100 |
| work_keys_str_mv | AT lijia atunnelsurfacediseasesdetectionalgorithmbasedonyolov4 AT qiuxinhua atunnelsurfacediseasesdetectionalgorithmbasedonyolov4 AT jiyuwen atunnelsurfacediseasesdetectionalgorithmbasedonyolov4 AT lijia tunnelsurfacediseasesdetectionalgorithmbasedonyolov4 AT qiuxinhua tunnelsurfacediseasesdetectionalgorithmbasedonyolov4 AT jiyuwen tunnelsurfacediseasesdetectionalgorithmbasedonyolov4 |