Improving the Application of Yolov7 Algorithm in Pin-Losing-Bolts Detection on Power Transmission Lines

Wenqing Zhao; Yutong Zhen

doi:10.54691/gt38rq02

Authors

Wenqing Zhao
Yutong Zhen

DOI:

https://doi.org/10.54691/gt38rq02

Keywords:

Fault Detection; Pin-losing-bolts; YOLOv7; Feature Fusion.

Abstract

A method for bolt deficiency detection on power transmission lines based on YOLOv7 is proposed to address the issues of low detection accuracy and high false negatives in current unmanned aerial vehicle (UAV) inspections. In the Neck module, the existing feature fusion structure is modified by directly combining deep and shallow feature information across adjacent scale feature maps. The prior box sizes in the original YOLOv7 are adjusted to better fit the dimensions and aspect ratios of bolt targets. The improved YOLOv7 algorithm enhances the learning capability of bolt feature information. Experimental results on the bolt deficiency dataset using this method show a 6% increase in precision and a 0.6% increase in mAP_0.5. The experiments demonstrate that the improved method enhances the detection ability of bolt deficiencies on power transmission lines and has practical value in intelligent inspections.

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