Surface defect classification of steels with a new semi-supervised learning method. (June 2019)

• Record Type: Journal Article
• Authors: Di, He; Ke, Xu; Peng, Zhou; Dongdong, Zhou
• Is Part Of: Optics and lasers in engineering. Volume 117(2019)
• Page Start: 40
• Page End: 48
• Publication Date: 2019-06
• Subjects: Surface inspection -- Defect detection -- Semi-supervised learning -- Convolutional autoencoder -- Generative adversarial networks; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2019.01.011
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: A semi-supervised learning method named CAE-SGAN is proposed to classify surface defects of steels. CAE-SGAN improves the performance of SGAN with limited training samples. When training the discriminator of SGAN, the decoder network of CAE is not truncated. CAE-SGAN is tested with sample images collected from three different steel production lines. CAE-SGAN provides a better way to apply deep learning methods to some industrial scenes. Abstract: Defect inspection is extremely crucial to ensure the quality of steel surface. It affects not only the subsequent production, but also the quality of the end-products. However, due to the rare occurrence and appearance variations of defects, surface defect identification of steels has always been a challenging task. Recently, deep learning methods have shown outstanding performance in image classification, especially when there are enough training samples. Since most sample images of steel surface are unlabeled, a new semi-supervised learning method is proposed to classify surface defects of steels. The new method is named CAE-SGAN, as it is based on Convolutional Autoencoder (CAE) and semi-supervised Generative Adversarial Networks (SGAN). CAE-SGAN first trains a stacked CAE through massive unlabeled data. Considering the appearance variations of defects, the passthrough layer is used to help CAE extract fine-grained features. After CAE is trained, the encoder network of CAE is reserved as the feature extractor and fed into a softmax layer to form a new classifier. SGAN is introduced for semi-supervised learning to further improve the generalization ability of the new method. The classifier is trained with images collected from real production lines and images randomly generated by SGAN. Extensive experiments are carried out with samples captured from different steel production lines, and the results indicate that CAE-SGAN had yielded best performances compared with traditional methods. Especially for hot rolled plates, the classification rate is improved by around 16%.