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Related Experiment Video

Updated: May 7, 2025

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An insulator target detection algorithm based on improved YOLOv5.

Bing Zeng1, Zhihao Zhou2, Yu Zhou3

  • 1Nanchang Institute of Technology, Nanchang, 330099, China. zengbing_whu@whu.edu.cn.

Scientific Reports
|January 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an improved YOLOv5 model for drone-based power line insulator detection. The enhanced algorithm significantly boosts detection accuracy and reduces computational load for real-time applications.

Keywords:
CSP-SCConvInsulatorLSKBlockRFBYOLOv5

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Drone inspections are crucial for power line maintenance.
  • Traditional object detection methods struggle with accuracy and efficiency for insulator detection.

Purpose of the Study:

  • To develop a more accurate and efficient insulator detection algorithm for power line inspections.
  • To address limitations of existing object detection models in terms of parameter count, accuracy, and miss rates.

Main Methods:

  • An improved YOLOv5 model incorporating a lightweight CSP-SCConv module in the backbone and neck networks.
  • Integration of a Receptive Field Block (RFB) and a Lattice Structured Kernel (LSKBlock) attention mechanism for enhanced feature extraction and fusion.
  • Utilized an [Formula: see text] loss function to improve bounding box accuracy and model robustness.

Main Results:

  • Achieved a mean Average Precision (mAP) of 95.60% with a reduced parameter count (18.36 M) and computational load (30.10G).
  • Demonstrated high Precision (P) of 88.10% and Recall (R) of 95.20%.

Conclusions:

  • The improved YOLOv5 model offers superior performance for insulator detection compared to traditional methods.
  • The algorithm is suitable for deployment on mobile devices, enabling real-time power line inspection.