A new lightweight deep neural network for surface scratch detection.

This paper aims to develop a lightweight convolutional neural network, , to realise automatic scratch detection for components in contact sliding such as those in metal forming. To this end, a large surface scratch dataset obtained from cylinder-on-flat sliding tests was used to train the with appropriate training parameters such as learning rate, gradient algorithm and mini-batch size. A comprehensive investigation on the network response and decision mechanism was also conducted to show the capability of the developed . It was found that compared with the existing networks, can realise an excellent classification accuracy of 94.16% with a much smaller model size and faster detection speed. Besides, outperformed other state-of-the-art networks when a public image database was used for network evaluation. The application of in an embedded system further demonstrated such advantages in the detection of surface scratches in sheet metal forming processes.