Transmission Line Defect Detection Method Integrating Attention Mechanism and Inverted Residual Design

Xueming Zhai; Chao Ling

doi:10.6919/ICJE.202504_11(4).0023

Authors

Xueming Zhai
Chao Ling

DOI:

https://doi.org/10.6919/ICJE.202504_11(4).0023

Keywords:

Transmission Line; Defect Detection; Attention Mechanism; Feature Fusion; Feature Extraction.

Abstract

To address the significant workload and high missed detection rate in the defect detection of transmission line inspection images, this paper proposes a defect detection method for transmission lines that balances real-time performance and accuracy. Based on the YOLOv8 model, the BoNAM module structure is designed to suppress background noise in transmission line images and reduce the dimensionality of input feature maps. The iRMB module is employed to efficiently extract backbone network features, while the SPD-Conv module is utilized to more precisely identify the missing locations of bolts and pins. Multiple ablation experiments were conducted on the improved algorithm components, with the enhanced method achieving an average precision of 86.9%, an improvement of 4.3% compared to the baseline model. Comparative experiments with mainstream object detection algorithms demonstrate that the proposed method outperforms others in terms of both accuracy and speed.

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