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%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cha, Y.J., Choi, W., Suh, G., Mahmoudkhani, S., Büyüköztürk, O.: Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput. Aided Civ. Infrastruct. Eng. 33(9), 731–747 (2018)
Chondronasios, A., Popov, I., Jordanov, I.: Feature selection for surface defect classification of extruded aluminum profiles. Int. J. Adv. Manuf. Technol. 83(1–4), 33–41 (2016)
Coren, S., Girgus, J.S., Day, R.: Visual spatial illusions: many explanations. Science 179(4072), 503–504 (1973)
Day, R.H.: Visual spatial illusions: a general explanation. Science 175(4028), 1335–1340 (1972)
Gan, J., Li, Q., Wang, J., Yu, H.: A hierarchical extractor-based visual rail surface inspection system. IEEE Sens. J. 17(23), 7935–7944 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
Ren, R., Hung, T., Tan, K.C.: A generic deep-learning-based approach for automated surface inspection. IEEE Trans. Cybern. 48(3), 929–940 (2017)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Schoonard, J.W., Gould, J.D.: Field of view and target uncertainty in visual search and inspection. Hum. Factors 15(1), 33–42 (1973)
Shumin, D., Zhoufeng, L., Chunlei, L.: Adaboost learning for fabric defect detection based on HOG and SVM. In: 2011 International Conference on Multimedia Technology, pp. 2903–2906. IEEE (2011)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Snyder, C.R.: Selection, inspection, and naming in visual search. J. Exp. Psychol. 92(3), 428 (1972)
Song, K., Yan, Y.: A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl. Surf. Sci. 285, 858–864 (2013)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: 31st AAAI Conference on Artificial Intelligence (2017)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Tabernik, D., Šela, S., Skvarč, J., Skočaj, D.: Segmentation-based deep-learning approach for surface-defect detection. J. Intell. Manuf. 31(3), 759–776 (2019). https://doi.org/10.1007/s10845-019-01476-x
Tao, X., Zhang, D., Ma, W., Liu, X., Xu, D.: Automatic metallic surface defect detection and recognition with convolutional neural networks. Appl. Sci. 8(9), 1575 (2018)
Wang, T., Chen, Y., Qiao, M., Snoussi, H.: A fast and robust convolutional neural network-based defect detection model in product quality control. Int. J. Adv. Manuf. Technol. 94(9–12), 3465–3471 (2018)
Zweig, M.H., Campbell, G.: Receiver-operating characteristic (roc) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39(4), 561–577 (1993)
Acknowledgement
This work is supported by the National Key Research and Development Plan of China (No. 2018YFC2000605).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-61616-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61615-1
Online ISBN: 978-3-030-61616-8
eBook Packages: Computer ScienceComputer Science (R0)