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October 23, 2025Engineering Research ExpressOpen Access

Application of Enhanced YOLOv8 in Multi-object Detection for Autonomous Inspection of Transmission Lines

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Authors

CLChuanyang LiuJLJingjing LiuYWYiquan Wu

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Overview

Analysis reveals improved robustness and defect detection in transmission lines, suggesting enhanced grid safety.

Key Points

  • Mean average precision of the enhanced YOLOv8 reached 86.73%, outperforming competitors significantly.
  • Analysis employed advanced modules including Faster-Block, GhostNet, and SPD-Conv for object detection improvement.
  • The method demonstrates superior performance at 38.1 frames per second for efficient transmission line inspections.
  • These findings highlight the importance of advanced detection systems for maintaining power grid stability.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68f9bad6d7353cfcfc68f370https://doi.org/10.1088/2631-8695/ae15e6
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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 YOLOv8-Based Approach for Foreign Object Detection on Transmission Lines2026
  2. 2Research on improved overhead transmission line defect detection algorithm based on YOLOv82024
  3. 3Improved YOLOv8-based insulator defect detection system for transmission lines2025 · 1 citations
  4. 4Efficient target detection method based on wavelet transform and progressive feature pyramid network: a case study of power grid inspection2026 · 1 citations
  5. 5Research on Multi-Object Tracking of Overhead Transmission Line Components Based on Improved YOLOv8+ ByteTrack2024 · 1 citations