Ensuring transmission line safety is crucial. Detecting insulator defects is a key task. UAV-based insulator detection faces challenges: com
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. [ABSTRACT FROM AUTHOR]
Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted
Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
