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Fog-Adaptive-YOLO: A lightweight model for insulator defect detection
Xiaoyuan Jin1,2, Yuzhen Zhao1,2, Wangyu Shen1,2
1Shaanxi Key Laboratory of Liquid Crystal Polymer Intelligent Display, Technological Institute of Materials & Energy Science (TIMES), Xijing University, Xi'an, China.
Plos One
|June 11, 2026
Summary
This study introduces Fog-Adaptive-YOLO, a lightweight network for detecting insulator defects in fog. The model enhances visibility and improves detection accuracy, offering a practical solution for unmanned aerial vehicle inspections.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Electrical Engineering
Background:
- Insulator defect detection using UAVs is challenging in foggy conditions due to complex backgrounds, small targets, and weather interference.
- Existing methods struggle with severe weather, impacting inspection reliability and safety.
Purpose of the Study:
- To develop a lightweight and effective deep learning model for insulator defect detection under foggy weather conditions.
- To improve the accuracy and efficiency of UAV-based infrastructure inspection in adverse environments.
Main Methods:
- Proposed Fog-Adaptive-YOLO, a lightweight detection network incorporating a FogEnhance module for noise suppression and feature enhancement.
- Optimized multi-scale feature extraction and aggregation using C3MSGR and C2fMSGR modules.
- Evaluated performance on self-constructed (InsDef-Fog) and public (IDID_FOG, WM-FOG, RTTS) datasets.
Main Results:
- Achieved 65.4% mAP50 on InsDef-Fog with only 2.74M parameters.
- Obtained 60.3% mAP50 on IDID_FOG and 80.2% mAP50 on WM-FOG.
- Demonstrated stable precision on the RTTS foggy dataset, showing robustness across different scenarios.
Conclusions:
- Fog-Adaptive-YOLO offers a favorable balance between detection accuracy and lightweight efficiency for foggy insulator defect detection.
- The proposed model is well-suited for practical UAV-based inspection tasks in adverse weather conditions.
- The network effectively suppresses fog noise and enhances weak defect features, outperforming existing methods.
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