Visual Detection of Steel Surface Defects Based on Transformer and Multi-attention

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Visual Detection of Steel Surface Defects Based on Transformer and Multi-attention

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[1]HAN Huijian,XING Huaiyu,ZHANG Yunfeng,et al.Visual Detection of Steel Surface Defects Based on Transformer and Multi-attention[J].Journal of Zhengzhou University (Engineering Science),2025,46(05):69-76.[doi:10.13705/j.issn.1671-6833.2025.05.009]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 05 Page number: 69-76 Column: Public date: 2025-08-10

Title:: Visual Detection of Steel Surface Defects Based on Transformer and Multi-attention

Author(s):: HAN Huijian; XING Huaiyu; ZHANG Yunfeng; ZHANG Rui; School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China

Keywords:: defect detection; attention mechanism; Transformer; hybrid sampling; DETR

CLC:: TP391TP18

DOI:: 10.13705/j.issn.1671-6833.2025.05.009

Abstract:: Addressing the challenges posed by the varying scales of steel surface defects and the limited multi-scale feature processing capabilitied and accuracy of existing detection algorithms, in this study a steel surface defect detection method that integrates hybrid sampling and multi-attention collaboration was proposed. Firstly, an efficient channel feature extraction backbone was constructed to emphasize defect feature extraction against the complex background of steel surfaces. Secondly, a dual-attention collaborative feature pyramid was introduced to expand the network′s receptive field, thereby enhancing the capture of multi-scale defect features and improving the detection performance for small targets. Finally, a Transformer-based hybrid sampling strategy was designed to dynamically perceive defect regions, thereby boosting the overall detection performance of the model. Experimental comparisons on the NEU-DET dataset revealed that, compared to the baseline DETR algorithm, the improved algorithm achieved a 6.1 percentage point increase in mean average precision, reaching 81.4%, thereby enhancing the model′s accuracy in detecting steel surface defects. Additionally, with a detection speed of 44.2 frame/s, the proposed algorithm strikes a commendable balance between detection speed and performance.

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Last Update: 2025-09-19