BAO W, YANG Y N, ZHANG J Y,et al.Multi-label classification method for transmission line inspection integrating label-guided attention and pairwise focal loss[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(5):71-79.

doi:10.16186/j.cnki.1673-9787.2025120094

Received:2025/12/29

Revised:2026/05/11

Published:2026-07-24

Multi-label classification method for transmission line inspection integrating label-guided attention and pairwise focal loss

Bao Wei, Yang Ya’nan, Zhang Jiayu

Zhengzhou Power Supply Company， State Grid Henan Electric Power Company， Zhengzhou 450052， Henan，China

Abstract: Objectives In response to the diverse equipment types and complex defect patterns in transmission line inspection， as well as the limitations of conventional object detection methods， including high annotation costs， large computational overheads， and limited performance in multi-label scenarios， a multi-label classification method for transmission line inspection integrating label-guided attention and pairwise focal loss is proposed. Methods Transmission line equipment and defect detection are reformulated as a multi-label classification task to eliminate the dependence on precise boundary annotations. To address feature semantic confusion and label co-occurrence interference， a method integrating label-guided attention and pairwise focal loss is proposed. The label-guided attention module employs learnable label prompts to guide the model toward discriminative regions， thereby reducing interference among labels. The pairwise focal loss models co-occurrence and mutual exclusion relationships at the label-pair level and incorporates a hard-example focusing mechanism to alleviate overfitting to statistical co-occurrence patterns， improving the discriminative capability for complex label combinations.  Results Comparative experiments on real-world inspection datasets against several mainstream baseline networks demonstrate that the proposed method achieves stable and significant improvements in multi-label classification performance. The mAP is improved by up to 7.65%， verifying the effectiveness and consistency of the proposed method across different network architectures. Conclusions The proposed multi-label classification method effectively addresses the coexistence of multiple labels in complex inspection scenarios and exhibits strong generalization capability and practical applicability. It provides an effective solution for achieving low annotation cost， high accuracy， and robust intelligent inspection.

Key words:transmission line inspection;multi-label classification;label-guided attention;pairwise focal loss;lightweight