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A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN.

Qiang Yang1,2, Song Ma1, Dequan Guo1

  • 1School of Automation, Chengdu University of Information Technology, Chengdu 610225, China.

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|September 9, 2023
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Summary
This summary is machine-generated.

This study introduces an improved Faster R-CNN model (FRRNet101-c) for detecting oil leaks in substation equipment, enhancing accuracy for small defects. Combined with intelligent robots, it aids workers in maintenance decisions for safer power transmission.

Keywords:
faster-RCNNintelligent inspection robotoil leakage detectionsmall object detectionsubstation equipments

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Substation equipment safety is crucial for reliable power transmission.
  • Current oil leakage detection methods struggle with small defects and lack intelligent robotic integration.
  • Intelligent inspection robots are essential for efficient substation maintenance.

Purpose of the Study:

  • To develop an accurate small object detection method for oil leakage in substations.
  • To integrate this method with intelligent inspection robots for enhanced substation monitoring.
  • To provide actionable maintenance recommendations for oil leakage incidents.

Main Methods:

  • Modified Faster R-CNN model using Resnet-101 feature extraction.
  • Implemented modifications include canceling downsampling and replacing large convolutional kernels with smaller ones to preserve information, especially for small objects.
  • Integrated the detection model with an intelligent inspection robot and developed a decision-making scheme for maintenance recommendations.

Main Results:

  • The proposed FRRNet101-c model demonstrated superior performance in oil leakage detection compared to baseline models.
  • Achieved a 6.3% improvement in Mean Average Precision (mAP) for overall detection.
  • Showcased a significant 12% improvement specifically in detecting small oil leakage objects.

Conclusions:

  • The FRRNet101-c model offers a highly effective solution for detecting oil leakage defects in substation equipment, particularly small ones.
  • The integration with intelligent inspection robots enhances substation inspection capabilities and supports timely maintenance decisions.
  • This approach contributes to ensuring the longevity of equipment and the stable operation of power systems.