Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels

Abstract

Due to the large intra-class variations and unbalanced training samples, the accuracy of existing algorithms used in defect classification of hot rolled steels is unsatisfactory. In this paper, a new hierarchical learning framework is proposed based on convolutional neural networks to classify hot rolled defects. Multi-scale receptive field is introduced in the new framework to extract multi-scale features, which can better represent defects than the feature maps produced by a single convolutional layer. A group of AutoEncoders are trained to reduce the dimension of the extracted multi-scale features which improve the generalization ability under insufficient training samples. Besides, to mitigate the deviation caused by fine-tuning the pre-trained model with images of different context, we add a penalty term in the loss function, which is to reconstruct the input image from the feature maps produced by the pre-trained model, to help network encode more effective and structured information. The experiments with samples captured from two hot rolled production lines showed that the proposed framework achieved a classification rate of 97.2% and 97% respectively, which are much higher than the conventional methods.