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Detail-semantic guide network based on spatial attention for surface defect detection with fewer samples

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Abstract

Surface defect detection is an important part of the process of product quality control in the industry. Automatic detection of surface defects based on machine learning is an up-and-coming research field, and there have been many successful cases. Deep learning has become the most suitable detection method for this task. Most algorithms require a large number of defect samples to achieve good results. However, defect samples in actual production are very limited. Although some unsupervised or semi-supervised methods can reduce training costs, their accuracy is difficult to guarantee, so they are difficult to be applied in industrial inspection. In this paper, we propose a detail-semantic guide network (DSGNet), which can achieve better result with fewer training samples. It is a two-stage neural network framework. In the first stage, we design a new semantic branch based on the modified residual shrinkage network and the proposed joined atrous spatial pyramid pooling (JASPP) module. This is the first time that residual shrinkage network is applied to defect detection and achieves good results. Also, we design a clear and efficient detail branch based on dense connection network. Specially, we propose a new detail-semantic guide module (DSGM), which can better integrate the feature information of the two branches. In the training phase, we propose a weight mask based on defect area to improve the ability of extracting small defects. We did experiments on four datasets and our method achieved excellent detection results even with only a small number of training samples.

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Funding

This work was supported by the Natural Science Foundation of China under Grant 51875113.

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Harbin Engineering University, Nantong Street, Harbin, 150000, Heilongjiang, China

    Yihan Meng, He Xu, Zhen Ma, Jiaqiang Zhou & Daquan Hui

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  1. Yihan Meng
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  2. He Xu
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  3. Zhen Ma
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  4. Jiaqiang Zhou
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Contributions

Yihan Meng: methodology, investigation, code, data analysis, writing-original draft; He Xu: supervision; Zhen Ma: review and editing; Jiaqiang Zhou, Daquan Hui: writing and editing.

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Correspondence to He Xu.

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Meng, Y., Xu, H., Ma, Z. et al. Detail-semantic guide network based on spatial attention for surface defect detection with fewer samples. Appl Intell 53, 7022–7040 (2023). https://doi.org/10.1007/s10489-022-03671-5

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