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Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection

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Abstract

Detection of surface defects in manufacturing systems is crucial for product quality. Detection of surface defects with high accuracy can prevent financial and time losses. Recently, efforts to develop high-performance automatic surface defect detection systems using computer vision and machine-learning methods have become prominent. In line with this purpose, this paper proposed a novel approach based on Depth-wise Squeeze and Excitation Block-based Efficient-Unet (DSEB-EUNet) for automatic surface defect detection. The proposed model consists of an encoder–decoder, the basic structure of the Unet architecture, and a Depth-wise Squeeze and Excitation Block added to the skip-connection of Unet. First, in the encoder part of the proposed model, low-level and high-level features were obtained by the EfficientNet network. Then, these features were transferred to the Depth-wise Squeeze and Excitation Block. The proposed DSEB based on the combination of Squeeze-Excitation and Depth-wise Separable Convolution enabled to reveal of critical information by weighting the features with a lightweight gating mechanism for surface defect detection. Besides, in the decoder part of the proposed model, the structure called Multi-level Feature Concatenated Block (MFCB) transferred the weighted features to the last layers without losing spatial detail. Finally, pixel-level defect detection was performed using the sigmoid function. The proposed model was tested using three general datasets for surface defect detection. In experimental works, the best F1-scores for MT, DAGM, and AITEX datasets using the proposed DSEB-EUNet architecture were 89.20%, 85.97%, and 90.39%, respectively. These results showed the proposed model outperforms higher performance compared to state-of-the-art approaches.

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Funding

This work was supported by the Inonu University Scientific Research Projects Coordination [Grant Number FDK-2021–2725].

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Bingol University, Bingol, Turkey

    Hüseyin Üzen

  2. Department of Software Engineering, Faculty of Engineering, Samsun University, Samsun, Turkey

    Muammer Turkoglu

  3. Department of Electrical-Electronics Engineering, Faculty of Engineering, Bingol University, Bingol, Turkey

    Muzaffer Aslan

  4. Department of Computer Engineering, Engineering Faculty, Inonu University, Malatya, Turkey

    Davut Hanbay

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  1. Hüseyin Üzen
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The Python code for our proposed method is available on the link: https://github.com/hn42/DSEB-EUnet.

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Üzen, H., Turkoglu, M., Aslan, M. et al. Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection. Vis Comput 39, 1745–1764 (2023). https://doi.org/10.1007/s00371-022-02442-0

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  • DOI: https://doi.org/10.1007/s00371-022-02442-0

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