Study on detection of dent defects of polariser based on deep convolutional generative adversarial network

The existing techniques of polariser detection only concern whether the polarisers have defects or not and do not classify them as specialised.In addition, lightweight CNN architectures proposed for defect classification of polarisers are based on limited samples.In order to attack the aforementioned issues, a novel grating imaging mechanism based on an adsorption transport platform is designed for a certain defect, dent.Multi-scale negative samples with dent defects and positive samples with other defects or not are expanded by a deep convolutional generative adversarial network (DCGAN).O sets, 64_10000 sets and 128_10000 sets (referred to as the original data, 64*64 generated data and 128*128 generated data) are trained on multiple convolutional neural networks (AlexNet, VGGNet, GoogLeNet, ResNet, SqueezeNet, MoblieNet, ShuffleNet) respectively, the obtained models are then validated on two new samples.Empirically show that ResNet obtained by 64G+128G perform better than others, classification accuracy rate of the new model is up to 94.94%.