| Issue |
ITM Web Conf.
Volume 45, 2022
2021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
|
|
|---|---|---|
| Article Number | 01013 | |
| Number of page(s) | 7 | |
| Section | Computer Technology and System Design | |
| DOI | https://doi.org/10.1051/itmconf/20224501013 | |
| Published online | 19 May 2022 | |
Angle steel tower bolt defect detection based on YOLO-V3
1
State Grid Anhui Electric Power Co., Ltd., Hefei, Anhui 230022, China
2
Department of Applied Mathematics, Wenzhou-Kean University, Wenzhou, Zhejian 325000, China
* Corresponding author: ahu0086@163.com
The bolts in the angle steel tower are seriously affected by corrosion and loss. This paper proposes a novel detection system based on YOLO-V3 to avoid the danger of traditional manual detection method for the bolt fault detection of the angle steel tower. A multi-scale convolution module is used to replace the ordinary convolution of original YOLO-V3 so as to obtain the spatial characteristics information of different scales in the image, and enhance the detection accuracy. The experimental results show that mAP of the proposed YOLO-SKIP network is 0.91. Our YOLO-SKIP model has achieved the best detection performance on the defective angle steel tower bolt data.
© The Authors, published by EDP Sciences, 2022
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