Scholarlink

<p>Steel defects can significantly diminish the corrosion resistance, wear resistance, and load-bearing capacity of the steel, leading to substantial financial losses. To effectively identify and locate steel surface defects, we propose a cross-scale feature fusion network. The process begins with the pre-processing of the input image through gray transformation and histogram equalization, followed by feature extraction using an enhanced backbone feature extraction network. Subsequently, a feature fusion network incorporating a gather-and-distribute (GD) structure is introduced to merge multi-scale feature maps, improving the robustness of information fusion across different scales. In the final stage, three detection heads of varying sizes undergo processing by a convolution module with a coordinate attention mechanism. The efficacy of the proposed method is validated using the Northeastern&#xa0;University surface defect database (NEU-DET) dataset, with experimental results demonstrating that the network achieves an 84.7% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5. Noteworthy contributors to the mAP of the proposed network include the image pre-processing module, the improved feature extraction network, the gather-and-distribute feature fusion network, and the detection network, contributing 4.1%, 2.9%, 2.6%, and 0.2%, respectively. The comparative experiments based on the attention mechanisms illustrate that the Squeeze-and-Excitation (SE) mechanism is the most suitable mechanism for the model proposed in this paper compared to other mainstream attention mechanisms. In comparison with other deep learning networks, our network demonstrates a significant enhancement in detection capability, showcasing superior performance in the identification of steel surface defects.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development of a Cross-scale Connection Network With Gather-and-distribute Structure for Steel Surface Defect Detection

  • 期刊论文

  • 发表日期：

    2026年1月11日

查看全文
  • Jiyao Wang,
  • Changjie Zheng,
  • Yuanrong Qi,
  • Shuangbao Shu,
  • Penghao Hu ,
  • Bingliang Guan

摘要

Steel defects can significantly diminish the corrosion resistance, wear resistance, and load-bearing capacity of the steel, leading to substantial financial losses. To effectively identify and locate steel surface defects, we propose a cross-scale feature fusion network. The process begins with the pre-processing of the input image through gray transformation and histogram equalization, followed by feature extraction using an enhanced backbone feature extraction network. Subsequently, a feature fusion network incorporating a gather-and-distribute (GD) structure is introduced to merge multi-scale feature maps, improving the robustness of information fusion across different scales. In the final stage, three detection heads of varying sizes undergo processing by a convolution module with a coordinate attention mechanism. The efficacy of the proposed method is validated using the Northeastern University surface defect database (NEU-DET) dataset, with experimental results demonstrating that the network achieves an 84.7% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5. Noteworthy contributors to the mAP of the proposed network include the image pre-processing module, the improved feature extraction network, the gather-and-distribute feature fusion network, and the detection network, contributing 4.1%, 2.9%, 2.6%, and 0.2%, respectively. The comparative experiments based on the attention mechanisms illustrate that the Squeeze-and-Excitation (SE) mechanism is the most suitable mechanism for the model proposed in this paper compared to other mainstream attention mechanisms. In comparison with other deep learning networks, our network demonstrates a significant enhancement in detection capability, showcasing superior performance in the identification of steel surface defects.