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Related Concept Videos

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
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Non-ohmic Devices00:51

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Related Experiment Videos

A Lightweight Insulator Defect Detection Model for Edge Computing Devices: PEBL-YOLO.

Hao Wang1, Jie Li1, Qi Xing1

  • 1School of Intelligent Science and Technology, Inner Mongolia University of Technology, Hohhot 010080, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces PEBL-YOLO, a lightweight model for detecting insulator defects on power lines. It offers high accuracy and efficiency for edge computing, improving power delivery safety.

Keywords:
PEBL-YOLOYOLOv11edge computinginsulator defect detectionlightweight object detection

Related Experiment Videos

Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Insulators are vital for power transmission lines but degrade over time due to environmental factors.
  • Current defect detection methods often lack efficiency and speed for edge computing deployment.

Purpose of the Study:

  • To develop a lightweight and efficient model for insulator defect detection suitable for CPU-based edge devices.
  • To improve the deployment efficiency and inference speed of insulator defect detection systems.

Main Methods:

  • Proposed PEBL-YOLO, a lightweight model based on YOLOv11 architecture.
  • Integrated PConv for enhanced feature extraction and fusion.
  • Reconstructed the neck with BiFPN and ECA for multi-scale feature aggregation.
  • Designed a lightweight shared decoupled detection head with parameter sharing and Group Normalization.

Main Results:

  • PEBL-YOLO achieved 95.0% Precision, 92.1% Recall, 94.4% mAP@0.5, and 53.6% mAP@0.5:0.95.
  • The model contains only 1.68 million parameters, demonstrating high parameter efficiency.
  • Achieved a favorable trade-off between detection accuracy and model complexity.

Conclusions:

  • PEBL-YOLO provides a practical and efficient solution for insulator defect detection in edge computing scenarios.
  • The model enhances the safety and stability of power delivery by enabling timely defect identification.
  • The lightweight design makes it suitable for deployment on resource-constrained edge devices.