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Power Defect Detection with Improved YOLOv12 and ROI Pseudo Point Cloud Visual Analytics.

Minglang Xu1, Jishen Peng1

  • 1School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces an advanced YOLOv12 framework for power equipment fault detection, enhancing accuracy in complex environments. The system generates 3D pseudo point clouds for intuitive defect visualization and analysis.

Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Power equipment inspection faces challenges with subtle defects and cluttered backgrounds.
  • Accurate fault detection is crucial for grid reliability and maintenance.

Purpose of the Study:

  • To develop an improved deep learning framework for multi-class power defect detection.
  • To enhance robustness and accuracy in complex inspection scenarios.
  • To provide intuitive visual analytics for defect-structure inspection.

Main Methods:

  • An improved YOLOv12 framework incorporating a Prior-Guided Region Attention (PG-RA) module and a Lightweight Residual Efficient Layer Aggregation Network (LR-RELAN).
  • A Dual-Spectrum Adaptive Fusion Loss (DSAF Loss) function for improved classification and regression.
Keywords:
ROI pseudo point cloudYOLOv12deep learningpower defect detectionvisual analytics

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  • Construction of Region of Interest (ROI) pseudo point clouds and comparison of denoising strategies (SOR, ROR).

    • Main Results:

    • The proposed framework demonstrates improved detection accuracy and robustness in complex scenes.
    • Real-time performance is maintained.
    • ROI pseudo point clouds offer an intuitive auxiliary view for practical defect-structure inspection.

    Conclusions:

    • The enhanced YOLOv12 framework effectively addresses challenges in power equipment fault detection.
    • The integration of PG-RA, LR-RELAN, and DSAF Loss leads to superior performance.
    • The ROI pseudo point cloud module enhances system interpretability and aids in practical defect analysis.