Defects detection for cable surface of cable-stayed bridge based on improved YOLOv5s network

Wang Pengfeng; Li Yuntang; Huang Yongyong; Zhu Wenkai; Lin Jie; Wang Binrui

doi:10.12086/oee.2024.240028

Article navigation > Opto-Electronic Engineering > 2024 Vol. 51 > No. 5 > 240028

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Wang P F, Li Y T, Huang Y Y, et al. Defects detection for cable surface of cable-stayed bridge based on improved YOLOv5s network[J]. Opto-Electron Eng, 2024, 51(5): 240028. doi: 10.12086/oee.2024.240028

Citation:

Wang P F, Li Y T, Huang Y Y, et al. Defects detection for cable surface of cable-stayed bridge based on improved YOLOv5s network[J]. Opto-Electron Eng, 2024, 51(5): 240028. doi: 10.12086/oee.2024.240028

Defects detection for cable surface of cable-stayed bridge based on improved YOLOv5s network

1.
College of Modern Science and Technology, China Jiliang University, Jinhua, Zhejiang 322002, China
2.
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China

Fund Project: Zhejiang Province Basic Public Welfare Research Program (LGF19E050002, LZ23E050002, LZ23E060002), and Special Funds for Basic Scientific Research Business Expenses of Zhejiang Provincial Universities (2020YW29)

More Information

^*Corresponding author: yuntangli@cjlu.edu.cn

Received Date 26 January 2024

Revised Date 19 April 2024

Accepted Date 24 April 2024

Published Date 25 May 2024

Abstract

Abstract

An improved YOLOv5s network for defects detection for the cable surface of cable-stayed bridge fast and accurately is proposed. This overcomes the problems of low efficiency and poor safety of manual inspection, slow and inaccuracy of existing target detection methods because of the interference of dirt leading to wrong and missed detections. The TRANS module is added to the backbone network of conventional YOLOv5s to obtain more features of a single image and improve defect detection accuracy. Moreover, the CSP module of the neck network is replaced by the GhostBottleneck module and ordinary convolution is replaced by depth-separable convolution to reduce parameters and improve the computational speed of the network. Furthermore, the SIOU loss function is used for suppressing the oscillation of the bounding box and improving the calculation accuracy of repeatability between the prediction and the real box, which can increase the model stability. The experiments show that mAP and FPS of improved YOLOv5s network are 94.26% and 68 frames per second, respectively. The performance is better than that of Faster-RCNN, YOLOv4, and conventional YOLOv5, and it can find the surface defect for the cable of the cable-stayed bridge accurately and timely.
- cable-stayed bridge cable /
- defects detection /
- YOLOv5s network /
- TRANS module /
- loss function

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References

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Overview

Overview

In recent years, the construction technology of large-span bridges has developed rapidly and its application has increased. As the main form of large-span bridges, the cable-stayed bridge has outstanding advantages such as beautiful appearance, strong seismic resistance, long span distance, low cost, and convenient construction. Therefore, it is widely used in bridges for crossing rivers or seas. As the main load-bearing component, the cable guarantees cable-stayed bridges being serviced safely. The interior of the cable is composed of high-strength steel wires and anti-corrosion coatings while the exterior is mainly protected by polyethylene or high-density polyethylene. Due to long-term exposure to the natural environment and affected by sunlight, wind, rain, and other factors, the protective layer of the cable is extremely easy to be erosion, deformation, cracking, and even peeling, which leads to the failure of the protective function. Furthermore, corrosion media and humid water mist entering the interior of the cable will cause steel wire corrosion and fracture. Therefore, regular cable detection is necessary to ensure bridge safety. Due to the low efficiency, high cost, and poor safety of manual detection of cable surface defects in cable-stayed bridges, existing target detection methods have low accuracy and slow speed, and are easily affected by cable surface dirt interference, resulting in false or missed detections. Therefore, an improved YOLOv5s network is proposed to achieve fast and accurate detection of cable surface defects. Add a TRANS module to the backbone network to obtain more features from a single image and improve defect detection accuracy. In the neck network, GhostBottleneck is used instead of the CSP module, and depthwise separable convolution is used instead of regular convolution to ensure detection accuracy while effectively reducing network parameters and significantly improving detection speed. Introducing the SIOU loss function to solve the problem of mismatch between the real and predicted boxes of small target defects, and improving the convergence speed and stability of the network. Using polyvinyl chloride pipes to simulate cable protection sleeves, constructing a dataset for experiments. The experimental results show that the mAP and FPS of the improved YOLOv5s network reach 94.26% and 68 frames per second, respectively, which are superior to Faster RCNN, YOLOv4, conventional YOLOv5, and other networks, meeting the requirements of surface defect detection accuracy and real-time performance for cable-stayed bridges.