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Updated: Jun 24, 2025

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Lightweight strip steel defect detection algorithm based on improved YOLOv7.

Jianbo Lu1, MiaoMiao Yu2, Junyu Liu3

  • 1Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China.

Scientific Reports
|June 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces SS-YOLO, a lightweight model for detecting steel strip surface defects. It significantly improves accuracy and reduces computational load, making quality control more efficient.

Keywords:
D-SimSPPFDeep learningLightweight networkStrip surface defect detectionYOLOv7

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Area of Science:

  • Materials Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate steel strip surface defect detection is vital for product quality.
  • Existing methods face challenges with large model sizes and computational complexity.

Purpose of the Study:

  • To develop a lightweight and efficient model for steel strip surface defect detection.
  • To enhance detection accuracy while reducing computational resources.

Main Methods:

  • Introduced SS-YOLO, a lightweight YOLOv7 variant.
  • Replaced CBS module with MobileNetv3 for reduced model size.
  • Integrated D-SimSPPF module with depth separable convolution and SimAM attention.
  • Applied SimAM attention in neck and prediction layers for feature extraction.

Main Results:

  • SS-YOLO achieved 97% mAP50 accuracy on the NEU-DET dataset, a 4.5% improvement over YOLOv7.
  • Reduced FLOPs by 79.3% and parameters by 20.7%.
  • Demonstrated a balance between detection accuracy, speed, and model size.

Conclusions:

  • SS-YOLO offers an effective solution for steel strip surface defect detection.
  • The model's lightweight nature and improved accuracy enhance industrial quality control.
  • This approach addresses the limitations of current complex defect detection algorithms.