SH-DETR: Enhancing steel surface defect detection and classification with an improved transformer architecture

Shouluan Wu, Hui Yang, Liefa Liao, Chao Song, Yating Fang and Yang Yang

PLOS ONE, 2025, vol. 20, issue 11, 1-31

Abstract: The detection of defects on steel surfaces constitutes a vital area of research in computer vision, characterized by its complexity and variety, which pose significant difficulties for accurate identification. In this context, we introduce a deep learning framework that combines multi-channel random coding with modules for multi-scale feature fusion to tackle the challenges of low recognition accuracy and insufficient classification power prevalent in conventional models. Our model capitalizes on the self-attention mechanism associated with the Transformer architecture, alongside the strong feature extraction capabilities of Convolutional Neural Networks (CNNs), to facilitate a combined improvement in performance. To start, we enhance the model’s feature extraction functionality by incorporating ResNet18 along with global self-attention. Next, we bring forth a novel improvement to the backbone network by adding a multi-channel shuffled encoding module, which effectively encodes various features through the interactions of different convolutional groups, thus minimizing the number of parameters. Additionally, we introduce a multi-feature fusion module UPC-SimAM (upsample concatenated Simple Parameter-Free Attention Module), which is free from parameter redundancy to bolster the model’s capacity to merge multi-scale features. Our experiments on the NEU-DET and GC10-DE datasets demonstrate that our model outperforms existing state-of-the-art techniques regarding detection efficiency. Specifically, the model registers a classification accuracy of 91.72%, an mAP@0.5 of 83.03%, and an mAP@0.5:0.95 of 45.55% on the NEU-DET dataset. On the GC10-DE dataset, it achieves a classification precision of 76.73%, an mAP@0.5 of 65.03%, and an mAP@0.5:0.95 of 32.46%. Through detailed ablation studies and visualization experiments, we affirm the considerable potential and benefits of the proposed SH-DETR model in the field of detecting defects on steel surfaces.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0334048

DOI: 10.1371/journal.pone.0334048

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