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May 29, 2026IET Image ProcessingOpen Access

MEA‐YOLO: Research on Adaptability and Robustness in Transmission Line Defect Detection for Complex Scenarios

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Authors

JXJing XieRWRui WangZLZhijian Liu

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Overview

Randomized trial demonstrates improved defect detection accuracy in challenging environments, suggesting enhanced practicality for real-world applications.

Key Points

  • The study aims to develop MEA-YOLO, an advanced detector that balances accuracy and computational cost for transmission line defect detection.
  • Developed the MAF-Enchancer module for feature extraction in degraded conditions.
  • Implemented SENetV2-based channel attention to enhance defect identification.
  • Created a dataset for transmission line defects under varied environmental conditions.
  • MEA-YOLO achieved a mean Average Precision (mAP) of 0.8031, exceeding YOLOv8n by 0.0502.
  • Demonstrated superior performance compared to traditional detectors and state-of-the-art methods.
  • Validated the model's generalizability via testing on an additional industrial defect dataset.

Cite This Study

Xie et al. (2026) studied this question.

synapsesocial.com/papers/6a192eb9fab5b468c4417fcdhttps://doi.org/10.1049/ipr2.70390
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