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Journal of Graphics ›› 2025, Vol. 46 ›› Issue (5): 950-959.DOI: 10.11996/JG.j.2095-302X.2025050950

• Image Processing and Computer Vision • Previous Articles     Next Articles

Adaptive feature fusion pyramid and attention mechanism-based method for transmission line insulator defect detection

ZHAI Yongjie(), ZHAI Bangchao, HU Zhedong, YANG Ke, WANG Qianming(), ZHAO Xiaoyu   

  1. Department of Automation, Huabei Electric Power University, Baoding Hebei 071003, China
  • Received:2024-12-06 Accepted:2025-02-12 Online:2025-10-30 Published:2025-09-10
  • Contact: WANG Qianming
  • About author:First author contact:

    ZHAI Yongjie (1972-), professor, Ph.D. His main research interest covers power vision. E-mail:zhaiyongjie@ncepu.edu.cn

  • Supported by:
    National Natural Science Foundation of China(62373151);Hebei Provincial Natural Science Foundation general project(F2023502010);Special Fund for Basic Scientific Research of Central Universities(2023JC006);Special Fund for Basic Scientific Research of Central Universities(2024MS136)

Abstract:

To address the challenges of complex background interference and varying defect region scales in transmission line insulator samples, a method for transmission line insulator defect detection based on an adaptive fusion feature pyramid and attention mechanism was proposed. First, an adaptive fusion module (AF) was introduced to process multi-scale feature information, which was integrated into the feature pyramid network to mitigate the inconsistencies of defect region scales in aerial images of insulators. Next, a defect feature refinement module (DFRM) based on an attention mechanism was designed to handle interference from complex background noise by expanding the receptive field and capturing the contextual features of defective regions. Finally, the improved algorithm was validated on a real-world transmission line insulator defect dataset. Experimental results demonstrated that the proposed method outperformed existing approaches in insulator defect detection, achieving a 5.7% improvement in accuracy compared to the baseline model. These findings offered an effective solution for intelligent inspection in power grid systems.

Key words: insulator defect, feature fusion, attention mechanism, object detection, multi-scale feature

CLC Number: