Angle steel tower bolt defect detection based on YOLO-V3

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...

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Bibliographic Details
Published inITM Web of Conferences Vol. 45; p. 1013
Main Authors Zhang, Jingfeng, Hu, Yuanwei, Ji, Shujun
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 2022
Subjects
Convolution
Fault detection
Image enhancement
Steel structures
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Summary: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.
ISSN:2271-2097
2431-7578
2271-2097
DOI:10.1051/itmconf/20224501013