A foreign body intrusion detection method for power lines based on Transformer

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

doi:10.1117/12.3008050

14 November 2023 A foreign body intrusion detection method for power lines based on Transformer

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

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 129341O (2023) https://doi.org/10.1117/12.3008050
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

The power system is constantly exposed to outdoor environments, which makes it susceptible to invasion by foreign body such as tree branches and garbage bags. Currently, most deep learning-based detection methods assume the presence of foreign body in the image, and there is still room for improvement in detection accuracy. In this paper, a foreign body detection method for the power system is proposed based on Inception-V3 and Trans-former. The method first classifies inspection images according to whether foreign bodies are present, and then detects foreign body that have invaded the power system. This method does not use pre-defined datasets and converts object detection into a direct bounding box prediction problem, which greatly optimizes existing detection methods. Experimental results on actual datasets show that our research effectively improves the accuracy and efficiency of foreign body detection compared to detection algorithms based on Faster R-CNN and YOLOv3.

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

Citation Download Citation

Wenqi Huang, Yang Wu, Zhuojun Cai, Ruiye Zhou, Qunsheng Zeng, Lingyu Liang, Jianing Shang, and Xuanang Li "A foreign body intrusion detection method for power lines based on Transformer", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 129341O (14 November 2023); https://doi.org/10.1117/12.3008050

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