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Space to depth convolution bundled with coordinate attention for detecting surface defects

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Abstract

Surface defects of steel plates unavoidably exist during the industrial production proceeding due to the complex productive technologies and always exhibit some typical characteristics, such as irregular shape, random position, and various size. Therefore, detecting these surface defects with high performance is crucial for producing high-quality products in practice. In this paper, an improved network with high performance based on You Only Look Once version 5 (YOLOv5) is proposed for detecting surface defects of steel plates. Firstly, the Space to Depth Convolution (SPD-Conv) is utilized to make the feature information transforming from space to depth, helpful for preserving the entirety of discriminative feature information to the greatest extent under the proceeding of down-sampling. Subsequently, the coordinate attention mechanism is introduced and embedded into the bottleneck of C3 modules to effectively enhance the weights of some important feature channels, in favor of capturing more important feature information from different channels after SPD-Conv operations. Finally, the Spatial Pyramid Pooling Faster module is replaced by the Spatial Pyramid Pooling Fully Connected Spatial Pyramid Convolution module to further enhance the feature expression capability and efficiently realize the multi-scale feature fusion. The experimental results on NEU-DET dataset show that, compared with YOLOv5, the mAP and mAP50 dramatically increase from 51.7, 87.0 to 61.4, 92.6%, respectively. Meanwhile, the frame rate of 250 FPS implies that it still preserves a well real-time performance. Undoubtedly, the improved algorithm proposed in this paper exhibits outstanding performance, which may be also used to recognize the surface defects of aluminum plates, as well as plastic plates, armor plates and so on in the future.

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Acknowledgements

This work was supported by the Natural Science Foundation of JiangXi Province under Grant No. 20224ACB201010 and the Postgraduate Student Innovation Fund of Jiangxi Province under Grant No.YC2023-S273.

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Authors and Affiliations

  1. College of Physics and Communication Electronics, Jiangxi Normal University, Nanchang, 330022, Jiangxi, China

    Wenqian Wan, Lei Wang, Bingbing Wang, Haoyang Yu, Kuijie Shi & Gang Liu

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  1. Wenqian Wan
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  2. Lei Wang
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  3. Bingbing Wang
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  4. Haoyang Yu
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  5. Kuijie Shi
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  6. Gang Liu
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Contributions

The overall study supervised by GL; Methodology, software, and preparing the original draft by WW; Review and editing by LW and BW; The results were analyzed and validated by HY and KS. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Gang Liu.

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Wan, W., Wang, L., Wang, B. et al. Space to depth convolution bundled with coordinate attention for detecting surface defects. SIViP 18, 4861–4874 (2024). https://doi.org/10.1007/s11760-024-03122-3

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