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October 11, 2025Information Technology And Control

Anormal Target Detection for Power Transmission Cable Drone Images Employing Improved YOLO10 and ESRGAN

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Authors

HZHaosen ZhaoLMLinghui MengYJYuyang Jiao

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Overview

Anomaly detection improves 6.62% using an advanced YOLO10 model in drone inspections, indicating enhanced image processing methods.

Key Points

  • Detection accuracy increases by 6.62% with the proposed method, improving operational decision-making.
  • The super-resolution technique utilizes YOLO10's backbone to enhance feature maps for better performance.
  • A new loss function allows for better adaptation to low pixel images, reducing training challenges.
  • Experimental results indicate a enhanced detection accuracy of 92.55% in power transmission cable inspections.

Cite This Study

Zhao et al. (2025) studied this question.

synapsesocial.com/papers/68e9b2e4ba7d64b6fc133129https://doi.org/10.5755/j01.itc.54.3.41460
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Also Consider

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

  1. 1An Enhanced YOLO Framework for Accurate Small-Target Cable Defect Detection2026
  2. 2Application of Enhanced YOLOv8 in Multi-object Detection for Autonomous Inspection of Transmission Lines2025
  3. 3An algorithm for power transmission line fault detection based on improved YOLOv4 model2024 · 15 citations
  4. 4An Optimized Algorithm for Transmission Line Anomaly Detection Based on Improved YOLOv11n2026
  5. 5Deep Learning Based Defect Detection Method for Overhead Transmission Wires2024