State-Space-Model-Guided Deep Feature Perception Network for Insulator Defect Detection in High-Resolution Aerial Images

Zhedong Hu, Bangchao Zhai, Zhenbing Zhao, Yongjie Zhai, Qianming Wang, Ke Yang

Abstract: With the advancement of high-resolution aerial imaging technology enabled by unmanned aerial vehicles (UAVs), insulator defect detection based on images has emerged as a key approach for intelligent inspection of overhead transmission lines. However, the insulator defective region detection (IDRD) task based on such imagery continues to face several critical challenges, including complex background interference, difficulty in detecting small-scale defects, and class imbalance in sample distribution. To tackle these challenges, we propose a state space model (SSM)-guided deep feature perception network for insulator defect detection (SGFP-YOLO). Specifically, the model introduces a bidirectional feature enhancement module (BFEM) and a dual-context feature refinement module (DFRM), both guided by a state-space framework, to enhance global features and capture local detail features. Additionally, to address the hard-easy sample imbalance problem in the detection process, we introduce focused balance loss, improving the performance of the model in both classification and regression tasks. Experiments are conducted on the self-constructed TLID dataset and the publicly available IDID dataset. The results show that SGFP-YOLO outperforms existing advanced models in several metrics, including mean average precision (mAP) @ 0.5 and $F1$ -score, especially in complex background and small-scale defect detection, providing an efficient and accurate solution for the intelligent detection of insulators in transmission lines.

External IDs:dblp:journals/tgrs/HuZZZWY25