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Updated: Jun 20, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Lightweight Visual Localization of Steel Surface Defects for Autonomous Inspection Robots Based on Improved YOLOv10n.

Jinwu Tong1, Xin Zhang1, Xinyun Lu1

  • 1Machine Vision and Deformation Monitoring Laboratory, Artificial Intelligence and Precision Medicine Research Laboratory, Engineering Training Center, School of Applied Technology, Nanjing Institute of Technology, Nanjing 211167, China.

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Summary
This summary is machine-generated.

KDM-YOLO enhances steel surface defect detection for autonomous robots by improving feature extraction and context modeling. This lightweight method achieves high accuracy and speed, crucial for real-time inspection tasks.

Keywords:
YOLOv10ndeep learningimage detectionsteel defectvisual positioning

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Area of Science:

  • Computer Vision
  • Robotics
  • Materials Science

Background:

  • Steel surface defect detection faces challenges with fine textures, scale variations, and background noise.
  • Lightweight detectors struggle to balance real-time performance with precise localization on mobile platforms.

Purpose of the Study:

  • To develop KDM-YOLO, an efficient visual localization and detection method for autonomous inspection robots.
  • To enhance steel surface defect detection by improving feature extraction, context modeling, and multi-scale fusion.

Main Methods:

  • Introduced KWConv for enhanced fine-grained texture and edge representation.
  • Employed C2f-DRB to expand receptive field and improve long-range dependency perception.
  • Integrated a multi-scale attention fusion (MSAF) module for adaptive spatial-semantic integration.

Main Results:

  • KDM-YOLO significantly improved Precision (91.0%), Recall (93.9%), and mAP@50 (95.4%) over the YOLOv10n baseline.
  • Achieved a compact model size (3.29 M parameters) and high inference speed (155.6 f/s).
  • Ablation studies confirmed the performance gains from individual modules and their combination.

Conclusions:

  • KDM-YOLO offers an effective balance between accuracy and computational efficiency for embedded navigation platforms.
  • The method provides a robust solution for online autonomous inspection and real-time localization of steel surface defects.