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<p>In the context of industrial hot-rolled strip steel surface defect detection, where the demands for real-time performance and classification accuracy are paramount, we present EAF-DenseNet121–a lightweight, enhanced model that incorporates edge-entropy attention mechanisms. At the inception of the DenseNet121 architecture, we incorporate a learnable Sobel-based edge extraction branch, which is designed to adaptively delineate defect contours with precision. We have designed an Entropy-Attention Fusion (EAF) module to further refine the model’s performance. This module constructs a four-dimensional tensor, integrating the primary feature map, edge map, and their corresponding local entropy maps. By applying dual-path channel-wise and spatial attention, we achieve a weighted fusion of information, thereby enriching the feature representation. The EAF module replaces three pivotal convolutional layers within the DenseNet framework–immediately following the initial convolution and subsequent to the first and second Transition layers. This replacement enhances feature representation with a negligible increase in additional parameters, leading to a substantial improvement in defect recognition and classification accuracy. Our experimental results, obtained on the NEU-DET dataset, reveal that the enhanced model achieves a classification accuracy of 99.17%, representing an improvement of 2.78% over the baseline. Furthermore, on the GC10-DET dataset, the model achieves a classification accuracy of 82.89%, further validating its strong generalization capabilities.</p>

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High precision classification of hot rolled strip steel surface defects using dual path features and entropy attention fusion

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月6日

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  • Yuanlin Wang,
  • Yiming Lu,
  • Fenghua Zhu,
  • Guangyue Du,
  • Zheng Li

摘要

In the context of industrial hot-rolled strip steel surface defect detection, where the demands for real-time performance and classification accuracy are paramount, we present EAF-DenseNet121–a lightweight, enhanced model that incorporates edge-entropy attention mechanisms. At the inception of the DenseNet121 architecture, we incorporate a learnable Sobel-based edge extraction branch, which is designed to adaptively delineate defect contours with precision. We have designed an Entropy-Attention Fusion (EAF) module to further refine the model’s performance. This module constructs a four-dimensional tensor, integrating the primary feature map, edge map, and their corresponding local entropy maps. By applying dual-path channel-wise and spatial attention, we achieve a weighted fusion of information, thereby enriching the feature representation. The EAF module replaces three pivotal convolutional layers within the DenseNet framework–immediately following the initial convolution and subsequent to the first and second Transition layers. This replacement enhances feature representation with a negligible increase in additional parameters, leading to a substantial improvement in defect recognition and classification accuracy. Our experimental results, obtained on the NEU-DET dataset, reveal that the enhanced model achieves a classification accuracy of 99.17%, representing an improvement of 2.78% over the baseline. Furthermore, on the GC10-DET dataset, the model achieves a classification accuracy of 82.89%, further validating its strong generalization capabilities.