Abstract
Surface defect detection is an indispensable step in the production process. Recent researches based on deep learning have paid primarily attention to improving accuracy. However, it is difficult to apply in real situation, because of huge number of parameters and the strict hardware requirements. In this paper, a lightweight fully convolutional neural network, named LFCSDD, is proposed. The parameters of our model are 11x fewer than baselines at least, and obtain the accuracy of 99.72% and 98.74% on benchmark defect datasets, DAGM 2007 and KolektorSDD, respectively, outperforming all the baselines. In addition, our model can process the images with different sizes, which is verified on the RSDDs with the accuracy of 97.00%.
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This work is supported by the National Key Research and Development Plan of China (No. 2018YFC2000605).
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Li, Y., Chen, Y., Gu, Y., Ouyang, J., Wang, J., Zeng, N. (2020). A Lightweight Fully Convolutional Neural Network of High Accuracy Surface Defect Detection. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12397. Springer, Cham. https://doi.org/10.1007/978-3-030-61616-8_2
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