Pin defect detection based on sparse features in transmission lines

Wenqi Huang; Ruiye Zhou; Qunsheng Zeng; Yang Wu; Zhuojun Cai; Jianing Shang; Lingyu Liang; Xuanang Li

doi:10.1117/12.3007987

14 November 2023 Pin defect detection based on sparse features in transmission lines

Wenqi Huang, Ruiye Zhou, Qunsheng Zeng, Yang Wu, Zhuojun Cai, Jianing Shang, Lingyu Liang, Xuanang Li

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 129340B (2023) https://doi.org/10.1117/12.3007987
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

Pin defects can seriously affect the safety of transmission lines. Because the pin is small, it is difficult to detect the pin defects. Most existing methods detect pin defects by increasing the number of feature layers or cascade mechanisms. However, since there is too much redundant information in the high-resolution feature map, it is difficult for existing methods to achieve a balance between high-resolution feature maps and inference speed. In this paper, we proposed Sparse RetinaNet to effectively relieve the contradiction between high-resolution feature layer and slow inference speed. Specifically, we introduce high-resolution features in the prediction, and proposed a sparse mechanism to sparse the features in the high-resolution feature layer so as to make use of high-resolution features without seriously affecting the inference speed. Extensive experiments on our own pin defect detection dataset show that our proposed method can significantly improve training efficiency and performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenqi Huang, Ruiye Zhou, Qunsheng Zeng, Yang Wu, Zhuojun Cai, Jianing Shang, Lingyu Liang, and Xuanang Li "Pin defect detection based on sparse features in transmission lines", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 129340B (14 November 2023); https://doi.org/10.1117/12.3007987

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KEYWORDS

Defect detection

Convolution

Power grids

Head

Ablation

Feature extraction

Feature fusion

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