Insulator recognition and self-explosion defect detection method based on improved YOLOv5 model

Yan Li; Jinqiao Du; Yong Yi; Wenfei Wan; Yusen Lin

doi:10.1117/12.2686610

10 August 2023 Insulator recognition and self-explosion defect detection method based on improved YOLOv5 model

Yan Li, Jinqiao Du, Yong Yi, Wenfei Wan, Yusen Lin

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127592L (2023) https://doi.org/10.1117/12.2686610
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

The insulator images taken by intelligent unmanned inspection equipment often have many small insulators in large-resolution scenes, and sparsely distributed self-destruct defects with complex backgrounds. These phenomena have brought great challenges to the efficient and accurate insulator recognition and defect detection based on insulator images. Therefore, this paper builds a large insulator image database, and proposes an insulator detection method through improving the YOLOv5 model by adding the smaller detection layer and optimizing the loss function to accurately classify and identify small insulators and detect their self-explosion defects. The proposed method can achieve a recognition accuracy rate of 97.8%, a recall rate of 96.2%, and a self-explosion defect detection accuracy rate of 95.1% in the constructed insulator database. The experimental results demonstrate that the proposed model has outstanding performance in terms of insulator recognition and the self-explosion defect detection, which can improve the efficiency of intelligent inspection of power grid system in practical application.

Citation Download Citation

Yan Li, Jinqiao Du, Yong Yi, Wenfei Wan, and Yusen Lin "Insulator recognition and self-explosion defect detection method based on improved YOLOv5 model", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127592L (10 August 2023); https://doi.org/10.1117/12.2686610

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KEYWORDS

Defect detection

Object detection

Databases

Detection and tracking algorithms

Dielectrics

Feature extraction

Performance modeling

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