A Semi-Supervised Deep Learning Approach for the Classification of Steel Surface Defects

Automatic surface inspection (ASI) to identify defects in manufactured items plays an important role in ensuring the production quality in industrial manufacturing processes. Various approaches have been proposed for this purpose, and the majority of them use supervised learning. Supervised learning requires labelled data for training. Obtaining a large amount of labelled data is a difficult, and time consuming task. On the other hand, Semi-supervised learning approaches become popular for ASI, as they make use of both labelled and unlabelled data. In this work, we propose a Convolutional Neural Network based semi-supervised learning approach for the recognition of steel surface defects. Our approach predicts the labels of the unlabelled data, and weights them based on their prediction confidence. These weighted samples are then used with their corresponding predicted labels, together with the labelled data for training the network. Our approach mainly differs from the existing approaches in the way the unlabelled samples are weighted when training the network. We propose to weight the samples based on how confidently they are predicted. We propose a margin-based approach to determine the prediction confidence. Experimental results on a public steel surface detection dataset (NEU surface defects) show that the proposed method can achieve a state-of-the-art accuracy of 99.15 ±0.08%, which is competitive compared to the performance achieved by fully supervised deep learning approaches, but ours with only 10% of labelled training data compared to the supervised learning approaches. In addition, comparison with recently proposed semi supervised learning approaches for ASI shows the effectiveness of our approach.