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Classification of industrial surface defects based on neural architecture search

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Abstract

Surface defect classification (SDC) is the visual inspection of the surface of an object to identify appearance defects. Efficient and accurate SDC is i mportant for improving the quality of industrial products. A manually designed convolutional neural network (CNN) is traditionally used for SDC. In this study, a simpler SDC scheme with a higher classification accuracy, named NAS-SDC, is developed based on the neural architecture search (NAS) technique. A max-pooling cell based on NASNet is introduced to reduce the search space and the number of network parameters, thus simplifying the candidate operators for the search. Two network architectures are proposed to stack the search candidates or the best cells. The proposed method can be used to automatically design an efficient CNN model for SDC on a specific dataset. Experimental results show that the proposed method can find the best cells in ~11 h using a single graphics processing unit (GPU) and achieves higher classification accuracies (99.98%, 99.8% and 99.26%) than state-of-the-art methods on the Northeastern University (NEU-CLS), DAGM, and bridge defect datasets. The number of network parameters used in the proposed method is only 0.35 M, and the average test time per sample is approximately 61 ms, thus achieving a balance between performance and speed.

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Acknowledgments

This research was supported in part by the National Natural Science Foundation of China (61941202), the Guangxi Natural Science Foundation (2018GXNSFBA281081), and the Guangxi Key Laboratory Fund of Embedded Technology and Intelligent System (2019-01-08, 2019-02-01).

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  1. Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guangxi, 541004, Guilin, People’s Republic of China

    Tiejun Yang, Tianshu Zhang & Lin Huang

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  1. Tiejun Yang
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  2. Tianshu Zhang
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Correspondence to Lin Huang.

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Yang, T., Zhang, T. & Huang, L. Classification of industrial surface defects based on neural architecture search. Multimed Tools Appl 80, 5187–5202 (2021). https://doi.org/10.1007/s11042-020-09968-2

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