Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels - He, Di; Xu, Ke; Wang, Dadong (Image and vision computing, 2019)
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. •We proposed a new framework based on Inception-V4 integrated with multi-scale respective field.•AutoEncoders are trained to reduce the dimension of the multi-scale features extracted by the pre-trained model.•A penalty term in the loss function to reconstruct the input image from the feature maps was added.•The methods were tested with two different samples from online surface inspection system of steels.