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September 10, 2025Measurement Science and Technology

MLS-YOLOv11: a strip steel surface defect detection model based on multi-layer feature fusion and shared convolution

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Authors

JLJingfa LeiJWJun WangYLYongling Li

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Overview

Proposed model enhances defect detection accuracy in steel strips, suggesting improved efficiency via shared convolution.

Key Points

  • MLS-YOLOv11 improves defect detection accuracy by 6.8% compared to previous models.
  • It features a multilayer fusion network and lightweight shared detection head for optimal performance.
  • The Shape-IoU loss function enhances bounding box regression precision, crucial for steel surface defect detection.
  • Detection speed reaches 127 fps while reducing computational load by approximately 7.7%.

Cite This Study

Lei et al. (2025) studied this question.

synapsesocial.com/papers/68c1c22554b1d3bfb60ef2b1https://doi.org/10.1088/1361-6501/aded2b
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects2024 · 21 citations
  2. 2Shape-IoU: More Accurate Metric considering Bounding Box Shape and Scale2023 · 77 citations
  3. 3Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression2020 · 4,241 citations
  4. 4Surface defect detection of steel strip at low resolution based on SAC-YOLOv52024 · 8 citations
  5. 5An End‐to‐End Steel Strip Surface Defects Recognition System Based on Convolutional Neural Networks2016 · 171 citations