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Structural Steel Products01:24

Structural Steel Products

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Structural steel products are created within a structural mill. The process begins with a beam blank that is reheated and then fed through a series of rollers. These rollers progressively shape the metal into its final form. Adjusting the spacings between the rollers allows for the production of different sections with the same nominal dimensions.
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Related Experiment Video

Updated: Feb 14, 2026

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
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LESSDD-Net: A Lightweight and Efficient Steel Surface Defect Detection Network Based on Feature Segmentation and

Jiayu Wu1, Longxin Zhang1, Xinyi Pu1

  • 1School of Computer Science and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, China.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
Summary

We developed LESSDD-Net, a lightweight network for steel surface defect detection. It significantly reduces computational costs and model size while improving accuracy, making it suitable for mobile devices.

Keywords:
attention mechanismdownsamplinglightweightsurface defect detection

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Area of Science:

  • Materials Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Steel surface defect detection is crucial for industrial quality and safety.
  • Current deep learning methods are computationally expensive, limiting mobile deployment.
  • There is a need for efficient and accurate defect detection models.

Purpose of the Study:

  • To propose a lightweight and efficient steel surface defect detection network (LESSDD-Net).
  • To reduce computational costs and model size for mobile applications.
  • To enhance detection accuracy compared to existing models.

Main Methods:

  • Introduced a lightweight downsampling module (CSPDDM).
  • Developed a lightweight attention mechanism (CCAttention).
  • Designed a lightweight C2f module (LP-C2f) for improved detection and reduced size.

Main Results:

  • LESSDD-Net improved mean average precision (mAP) by 3.19% over YOLO11n.
  • Reduced model parameters by 39.92% and computational costs by 20.63% compared to YOLO11n.
  • Achieved top detection accuracy with the lowest model complexity among mainstream object detection models.

Conclusions:

  • LESSDD-Net offers a highly accurate and computationally efficient solution for steel surface defect detection.
  • The proposed network is suitable for deployment on resource-constrained devices like mobile phones.
  • The novel modules (CSPDDM, CCAttention, LP-C2f) contribute to the network's superior performance and efficiency.