Abstract
The classification method of steel surface defects with high performance and easy to be embedded in the detection equipment is one of the keys to ensure the quality of hot rolled strip. However, the development of deep convolutional neural networks (CNNs) in many real-world applications is largely hindered by their high computational cost, especially in industrial production, although it has good classification accuracy compared with machine learning-based methods in image recognition. Therefore, in this work, we present a lightweight network FCCNet based on the convolutional neural network to facilitate its application in the detection system. To compensate for the accuracy loss caused by the network downsizing, a knowledge distillation (KD) method using a larger trained network (teacher network) to teach a smaller network (student network) is adopted to improve the performance of our model. As a result, our method achieves a classification accuracy of 99.44%, precision of 99.46%, recall of 99.45%, and an F1 score of 99.45% on the NEU-CLS dataset, using only 0.03 MB parameters. These results show that the FCCNet is lighter than other existing classic CNNs with good performance for surface defects classification of hot-rolled steel strip, and it has the potential to be applied in the actual production line.
