An automatic defect detection system based on deep learning for fasteners in the power system

Tao Yang, Zhongjing Ma, Tianyu Wang, Jiaxin Fu, Suli Zou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Fasteners on transmission lines are widely used in various power connection components, and the loosening and missing of fastener cotter pins can trigger problems such as falling off of components, which has a huge potential safety hazard. It is necessary to propose an automatic fastener defect detection method to ensure the stable operation of the transmission line. In this paper, a three-stage cascade system for automatic detection of fastener defects is constructed in a coarse-to-fine mode, including fastener location network (FLN), feature refinement network (FRN), and defect diagnosis network (DDN). First, the proposed FLN improves the YOLOV4 model based on a blurred module to locate fasteners. Then, the proposed FRN is applied to extract the semantic information of the fasteners and refine the features. Finally, the proposed DDN is used to classify the cotter pin defects. To verify the adaptability and accuracy of the method, a considerable amount of experiments and analyses have been performed, and the results show that the proposed detection method reaches an accuracy of 98.4% and testing speed of 0.64s, achieving state-of-the-art (SOTA) performance.

Original languageEnglish
Title of host publicationProceedings of the 41st Chinese Control Conference, CCC 2022
EditorsZhijun Li, Jian Sun
PublisherIEEE Computer Society
Pages6599-6604
Number of pages6
ISBN (Electronic)9789887581536
DOIs
Publication statusPublished - 2022
Event41st Chinese Control Conference, CCC 2022 - Hefei, China
Duration: 25 Jul 202227 Jul 2022

Publication series

NameChinese Control Conference, CCC
Volume2022-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference41st Chinese Control Conference, CCC 2022
Country/TerritoryChina
CityHefei
Period25/07/2227/07/22

Keywords

  • Cascade System
  • Deep Learning
  • Fastener Detection
  • Power Transmission Line

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Yang, T., Ma, Z., Wang, T., Fu, J., & Zou, S. (2022). An automatic defect detection system based on deep learning for fasteners in the power system. In Z. Li, & J. Sun (Eds.), Proceedings of the 41st Chinese Control Conference, CCC 2022 (pp. 6599-6604). (Chinese Control Conference, CCC; Vol. 2022-July). IEEE Computer Society. https://doi.org/10.23919/CCC55666.2022.9902337