Research on self-explosion defect detection of insulator based on improved YOLOv7

Dawei Zhang; Yang Yang

doi:10.1117/12.3025636

28 February 2024 Research on self-explosion defect detection of insulator based on improved YOLOv7

Dawei Zhang, Yang Yang

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130710F (2024) https://doi.org/10.1117/12.3025636
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

Insulator defect detection of transmission lines is one of the important tasks in the operation of power systems. Therefore, the self-explosion defect of insulators has become an important task in power inspection. Therefore, this article proposes an improved YOLOv7 based algorithm for detecting self-exploding defects in insulators. Firstly, select the loss function WIoU to reduce the missed detection rate; Then, use the CARAFE module for feature recombination to improve the detection accuracy of the model; Finally, the CBAM attention mechanism is cited to further improve the model performance. Through experimental verification, the improved YOLOv7 model has good detection performance, with mAP@.5% and Recall increased by 1.3% and 2.4% respectively, making it more suitable for detecting self-exploding defects in insulators.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dawei Zhang and Yang Yang "Research on self-explosion defect detection of insulator based on improved YOLOv7", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130710F (28 February 2024); https://doi.org/10.1117/12.3025636

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KEYWORDS

Object detection

Defect detection

Detection and tracking algorithms

Target detection

Data modeling

Ablation

Feature extraction

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