FIAEPI-KD: A novel knowledge distillation approach for precise detection of missing insulators in transmission lines

Abstract

Ensuring transmission line safety is crucial. Detecting insulator defects is a key task. UAV-based insulator detection faces challenges: complex backgrounds, scale variations, and high computational costs. To address these, we propose FIAEPI-KD, a knowledge distillation framework integrating Feature Indicator Attention (FIA) and Edge Preservation Index (EPI). The method employs ResNet and FPN for multi-scale feature extraction. The FIA module dynamically focuses on multi-scale insulator edges via dual-path attention mechanisms, suppressing background interference. The EPI module quantifies edge differences between teacher and student models through gradient-aware distillation. The training objective combines Euclidean distance, KL divergence, and FIA-EPI losses to align feature-space similarities and edge details, enabling multi-level knowledge distillation. Experiments demonstrate significant improvements on our custom dataset containing farmland and waterbody scenarios. The RetinaNet-ResNet18 student model achieves a 10.5% mAP improvement, rising from 42.7% to 53.2%. Meanwhile, the Faster R-CNN-ResNet18 model achieves a 7.4% mAP improvement, rising from 42.7% to 50.1%. Additionally, the RepPoints-ResNet18 model achieves a 7.7% mAP improvement, rising from 49.6% to 57.3%. These results validate the effectiveness of FIAEPI-KD in enhancing detection accuracy across diverse detector architectures and backbone networks. On the MSCOCO dataset, FIAEPI-KD outperformed mainstream distillation methods like FKD and PKD. Ablation studies confirmed FIA’s role in feature focus and EPI’s edge difference quantification. Using FIA alone increased RetinaNet-ResNet50’s mAP by 0.9%. Combined FIA+EPI achieved a total 3.0% improvement, the method utilizes a lightweight student model for efficient deployment, providing an effective solution for detecting insulation defects in transmission lines.