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December 10, 2025SensorsOpen Access

MRA-YOLOv8: A Transmission Line Fault Detection Algorithm Integrating Multi-Scale Feature Fusion

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Authors

SHShuai HaoJLJing LiXMXu Ma

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Overview

Algorithm improves fault detection accuracy in transmission line inspections by reducing class imbalance and enhancing feature extraction.

Key Points

  • This research aims to enhance fault detection accuracy in transmission line inspections amidst background interference and occlusions.
  • Utilized a YOLOv8 network as the baseline framework.
  • Incorporated a self-attention mechanism to enhance detection of occluded targets.
  • Introduced a Multi-scale Attention Aggregation module to boost feature extraction capabilities.
  • Optimized the bounding box loss function to address class imbalance issues.
  • Achieved an average detection precision of 92.5%.
  • Demonstrated a recall rate of 90.9%.
  • Improved detection performance compared to traditional methods.

Cite This Study

Hao et al. (2025) studied this question.

synapsesocial.com/papers/69401d412d562116f28f830ehttps://doi.org/10.3390/s25247508
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1DM-YOLO: Transmission Line Fault Detection Based on Dynamic Multi-scale Convolution and Attention Mechanism2025 · 3 citations
  2. 2PGE-YOLO: A Multi-Fault-Detection Method for Transmission Lines Based on Cross-Scale Feature Fusion2024 · 19 citations
  3. 3An algorithm for power transmission line fault detection based on improved YOLOv4 model2024 · 15 citations
  4. 4TLDD-YOLO: An Improved YOLO for Transmission Line Component and Defect Detection2026 · 1 citations
  5. 5Application of Enhanced YOLOv8 in Multi-object Detection for Autonomous Inspection of Transmission Lines2025 · 4 citations