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<p>In order to address the challenges of deployment difficulties and low small-object detection efficiency in current deep learning-based defect detection models on terminal devices with limited computational capacity, this paper proposes a lightweight steel surface defect detection model, Pyramid-based Small-target Fusion YOLO (PSF-YOLO), based on an improved YOLOv11n object detection framework. The model employs a low-parameter Ghost convolution (GhostConv) to substantially reduce the required computational resources. Additionally, the traditional feature pyramid network structure is replaced with a Multi-Dimensional-Fusion neck (MDF-Neck) to enhance small-object perception and reduce the number of model parameters. Moreover, to achieve multi-dimensional integration in the neck, a Virtual Fusion Head is utilized, and the design of an Attention Concat module further improves target feature extraction, thereby significantly enhancing overall detection performance. Experimental results on the GC10-DET+ dataset demonstrate that PSF-YOLO reduces model parameters by 25% while achieving improvements of 3.2% and 3.3% in <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_16619_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="52" /> </InlineMediaObject> <EquationSource Format="TEX">\(mAP_{50}\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_16619_Article_IEq3.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="73" /> </InlineMediaObject> <EquationSource Format="TEX">\(mAP_{50-95}\)</EquationSource> </InlineEquation>, respectively, compared to the baseline model. This approach offers valuable insights and practical applicability for deploying defect detection models on terminal devices with limited computational resources.</p>

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A lightweight YOLOv11-based framework for small steel defect detection with a newly enhanced feature fusion module

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月2日

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  • Yongyao Wang,
  • Haiyang Sun,
  • Kai Luo,
  • Quanfu Zhu,
  • Haofei Li,
  • Yuyang Sun,
  • Zhenjie Wu,
  • Gang Wang

摘要

In order to address the challenges of deployment difficulties and low small-object detection efficiency in current deep learning-based defect detection models on terminal devices with limited computational capacity, this paper proposes a lightweight steel surface defect detection model, Pyramid-based Small-target Fusion YOLO (PSF-YOLO), based on an improved YOLOv11n object detection framework. The model employs a low-parameter Ghost convolution (GhostConv) to substantially reduce the required computational resources. Additionally, the traditional feature pyramid network structure is replaced with a Multi-Dimensional-Fusion neck (MDF-Neck) to enhance small-object perception and reduce the number of model parameters. Moreover, to achieve multi-dimensional integration in the neck, a Virtual Fusion Head is utilized, and the design of an Attention Concat module further improves target feature extraction, thereby significantly enhancing overall detection performance. Experimental results on the GC10-DET+ dataset demonstrate that PSF-YOLO reduces model parameters by 25% while achieving improvements of 3.2% and 3.3% in \(mAP_{50}\) and \(mAP_{50-95}\) , respectively, compared to the baseline model. This approach offers valuable insights and practical applicability for deploying defect detection models on terminal devices with limited computational resources.