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Related Experiment Video

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An Improved CenterNet Model for Insulator Defect Detection Using Aerial Imagery.

Haiyang Xia1, Baohua Yang1,2, Yunlong Li1

  • 1School of Information and Computer, Anhui Agricultural University, Hefei 230036, China.

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|April 23, 2022
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Summary
This summary is machine-generated.

This study introduces MobileNet_CenterNet for improved insulator and defect detection in power inspections using unmanned aerial vehicles (UAVs). The model enhances accuracy and real-time performance, making power grid inspections more efficient and reliable.

Keywords:
CenterNetinsulator defect detectionunmanned aerial vehicle (UAV)

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Power inspection relies on accurate and timely detection of insulators and defects.
  • Current unmanned aerial vehicle (UAV) based methods face challenges with low accuracy and poor real-time performance.

Purpose of the Study:

  • To develop an improved insulator detection model for power inspection.
  • To enhance both the accuracy and real-time capabilities of UAV-based defect detection.

Main Methods:

  • Proposed MobileNet_CenterNet model, replacing Resnet-50 with lightweight MobileNet V1 for faster feature extraction.
  • Integrated a Convolutional Block Attention Module (CBAM) to improve small target detection.
  • Implemented transposed convolution modules for enhanced semantic and positional information restoration.
  • Trained and validated the model on custom (ID) and public (CPLID) insulator datasets.

Main Results:

  • MobileNet_CenterNet demonstrated a 12.2% increase in detection accuracy compared to the original CenterNet.
  • Inference speed improved by 1.1 FPS (CPU) and 4.9 FPS (GPU).
  • Model size was reduced by 37 MB, indicating significant efficiency gains.

Conclusions:

  • The MobileNet_CenterNet model offers superior real-time performance and robustness for insulator and defect detection.
  • This advancement is crucial for optimizing power inspection processes using UAVs.
  • The model's improved accuracy and speed contribute to more efficient and reliable power grid maintenance.