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Related Experiment Video

Updated: May 31, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

A multi-class defect detection method for substations based on the improved YOLOv10n.

Long Huang1, Kangning Li1, Tianren Fu1

  • 1Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., CSG, Guangzhou, Guangdong, China.

Frontiers in Artificial Intelligence
|May 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces YOLO-SMALLNET, an advanced defect detection algorithm for power substations. It significantly improves the accuracy of identifying small, blurred defects, enhancing electrical grid reliability.

Keywords:
YOLOv10ncontent-guided attentiondetail information extraction convolutionsubstation defect detectionweighted hybrid fusion pyramid network

Related Experiment Videos

Last Updated: May 31, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Automated inspection of power substation equipment is crucial for grid reliability.
  • Detecting small, blurred defects in complex backgrounds presents a significant challenge.

Purpose of the Study:

  • To enhance the localization accuracy of small, fuzzy defects in power substation equipment.
  • To develop a robust algorithm for real-time automated inspection.

Main Methods:

  • Proposed YOLO-SMALLNET algorithm based on YOLOv10n.
  • Incorporated Detail Information Extraction Convolution, low-level feature fusion, Weighted Hybrid Fusion Pyramid Network, and Content-Guided Attention.
  • Replaced strided convolutions to preserve fine-grained information and reduce feature loss.

Main Results:

  • YOLO-SMALLNET demonstrated improvements in Precision (+7.3%), Recall (+8.2%), mAP@0.5 (+3.9%), and mAP@0.5:0.95 (+3.3%) compared to the baseline.
  • Effectively reduced false and missed detections of small defect regions.

Conclusions:

  • The proposed YOLO-SMALLNET algorithm is effective for real-time automated inspection of power substation equipment.
  • Achieved superior performance in detecting challenging small and blurred defects.