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Enhanced Multi-Scale Defect Detection in Steel Surfaces via Innovative Deep Learning Architecture.

Zhaoxuan Zhou1, Yan Cao2

  • 1School of Mechanical and Electrical Engineering, Xi'an Technological University, Xi'an 710021, China.

Sensors (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

This study introduces CTG-YOLO, a deep learning model for steel surface defect detection. It significantly improves accuracy, offering a more reliable solution for industrial quality control.

Keywords:
CBY parallel network structureTFF-PANetdeep learningobject detectionsteel surface defects

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Area of Science:

  • Materials Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Steel surface defects critically affect industrial product quality and safety.
  • Existing defect detection methods are often inefficient and limited in scope.

Purpose of the Study:

  • To develop an advanced deep learning model for enhanced multi-scale steel surface defect detection.
  • To improve the accuracy and efficiency of identifying surface imperfections in steel.

Main Methods:

  • An innovative deep learning architecture, CTG-YOLO, was designed.
  • The model integrates a CBY parallel network, TFF-PANet neck, and GS-Head detection head.
  • Feature extraction and fusion capabilities were enhanced.

Main Results:

  • The CTG-YOLO model achieved high mean Average Precision (mAP) scores: 76.55% on NEU-DET and 69.94% on GC10-DET.
  • Performance improvements of 3.72% and 3.14% over the original YOLOv8s were recorded.
  • Superior feature extraction and fusion were demonstrated.

Conclusions:

  • The CTG-YOLO model offers a robust and accurate solution for steel surface defect detection.
  • This research lays a strong foundation for practical industrial defect detection applications.
  • The developed deep learning approach enhances quality control in steel manufacturing.