Convolutional Network Research for Defect Identification of Productor Appearance Surface

Abstract

The accurate and rapid identification of surface defects is an important element of product appearance quality evaluation, and the application of deep learning for surface defect recognition is an ongoing hot topic. In this paper, a lightweight KD-EG-RepVGG network based on structural reparameterization is designed for the identification of surface defects on strip steel as an example. In order to improve the stability and accuracy in the recognition of strip steel surface defects, an efficient attention network was introduced into the network, and then a Gaussian error linear activation function was applied in order to prevent the neurons from being set to zero during neural network training, leaving neuron parameters without being updated. Finally, knowledge distillation is used to transfer the knowledge of the RepVGG-A0 network to give the lightweight model better accuracy and generalization capability. The outcomes of the experiments indicate that the model has a computational and parametric volume of 22.3 M and 0.14 M, respectively, in the inference phase, a defect recognition accuracy of 99.44% on the test set, and a single image detection speed of 2.4 ms, making it more suitable for deployment in real engineering environments.