BAO W, YANG Y N, et al． Multi-Label classification method for transmission line inspection via label-guided attention and pairwise focal loss[J]. Journal of Henan Polytechnic University( Natural Science) ， doi: 10.16186/j.cnki.1673-9787.  （ 2025120094 ）..

doi: 10.16186/j.cnki.1673-9787. （ 2025120094 ）..

Received：2025-12-29

Revised： 2026-05-11

Online： 2026-06-04

Multi-Label classification method for transmission line inspection via label-guided attention and pairwise focal loss (Online)

BAO Wei, YANG Yanan

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

Abstract: Objectives In view of the diverse equipment and complex defects in power line inspections, conventional detection approaches face issues such as high annotation costs and excessive computational demands. This study develops a robust and efficient intelligent recognition framework that achieves a superior trade-off between precision and speed, aiming to facilitate practical automated inspections in complex scenes, Methods In this work, transmission line equipment and defect detection is reformulated as a multi-label classification task, thereby eliminating the reliance on precise bounding box annotations. To address feature-level semantic ambiguity and label co-occurrence interference, we propose a unified framework that integrates label-guided attention with a pairwise focal loss. Specifically, the label-guided attention introduces learnable label prompts to steer the model toward discriminative regions relevant to each label, thus mitigating inter-label interference at the feature level. Meanwhile, the pairwise focal loss explicitly models co-occurrence and mutual exclusivity among label pairs, and incorporates a hard-example focusing mechanism to alleviate overfitting to statistical co-occurrence patterns, thereby enhancing the model’s ability to distinguish complex label combinations. Results The proposed approach consistently outperforms several state-of-the-art baselines on real-world inspection datasets. With a maximum mAP gain of 7.15%, our method demonstrates superior multi-label classification performance and maintains remarkable consistency across diverse architectural backbones. Conclusions Experimental results demonstrate that the proposed multi-label classification method effectively addresses the challenge of label co-occurrence in complex inspection scenarios, exhibiting strong generalization capability and practical applicability. It provides an effective solution for achieving low annotation cost, high accuracy, and robust performance in intelligent inspection systems.

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