Authors
Xiuyang Yuan, Zhiyu Shang, Jiaman Fang, Han Zhang, Yuhang Ou
Published in
PloS one. Volume 21. Issue 8. Pages e0356260. Epub Aug 26, 2026.
Abstract
Addressing challenges in UAV power line inspection-where insulator defect detection models are prone to environmental interference, insufficient feature representation, and difficulty balancing lightweight requirements-this study develops a lightweight image defect detection model that integrates high accuracy with strong robustness. An enhanced algorithm based on YOLOv8n is proposed. MobileNetV4 is adopted as the lightweight backbone, CBAM is introduced to enhance defect feature representation, ABIFPN is designed for multiscale bidirectional feature fusion, and SIoU is employed to improve localization accuracy. A multi-weather dataset containing 3,851 images of self-shattered and damaged insulators under rainy, snowy, foggy, overcast, and varying-exposure conditions was constructed using real and synthesized images. The dataset was divided into training, validation, and test sets at a ratio of 7:2:1. Across five independent experiments, the proposed model improved precision, recall, [email protected], and [email protected]:0.95 by 2.43, 2.32, 2.43, and 5.47 percentage points, respectively, compared with the baseline. With a model size of only 7.01 MB, demonstrated better overall detection performance than YOLOv5n and YOLOv7-tiny. These results indicate its potential for UAV-mounted edge-based transmission-line inspection. However, some weather samples were synthetically generated, and more real-world data will be incorporated in future work.
PMID:
42647550
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.
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