[1]ZHAO Wenqing,ZHANG Le.Synergistic decoupling and guidance for transmission line bolt defect detection[J].CAAI transactions on intelligent systems,2026,21(5):1142-1153.[doi:10.11992/tis.202509002]
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Synergistic decoupling and guidance for transmission line bolt defect detection

CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 5 Page number: 1142-1153 Column: 学术论文—机器学习 Public date: 2026-09-05
Title:
Synergistic decoupling and guidance for transmission line bolt defect detection
Author(s):
ZHAO Wenqing1; 2;  ZHANG Le1
1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
2. Hebei Key Laboratory of Knowledge Computing for Energy & Power, North China Electric Power University, Baoding 071003, China
Keywords:
bolt; defect detection; power line inspection; small object detection; feature fusion; decoupled pyramid pooling; deep learning; one-stage detector
CLC:
TP391
DOI:
10.11992/tis.202509002
Abstract:
This paper proposes a novel detection method integrating decoupling and guidance strategies to address the challenges of detecting small fasteners in UAV imagery, including detail loss, insufficient contextual information, and conflicts in multi-scale feature fusion. The method mainly consists of the following three modules: a high-frequency detail-guided feature enhancement (HFDG) module that leverages high-frequency components to drive a gating mechanism, thereby enhancing the representation of faint targets; a decoupled context and salient pyramid pooling (DCSPP) module that captures peak responses and regional statistics in parallel to achieve complementary information; and an asymmetric guidance fusion pyramid network (AGF-PN) that mitigates semantic conflicts via asymmetric path shunting and a detail-guided fusion strategy. Experiments on a self-built bolt defect dataset demonstrate superior performance, achieving 93.9% mAP@50 and 63.4% mAP@.5:.95, surpassing the YOLOv11s baseline by 3.1 and 2.9 percentage points, respectively. The proposed method also demonstrates strong generalization on the public RSOD and DIOR datasets.
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