Research on defect pattern recognition of Light Guide Plate based on Deep Learning semantic Segmentation

Abstract

Abstract Because the defects of the light guide plate are still extremely small under the image taken by the high-resolution industrial camera, and the characteristics of different defects are different, as well as the texture characteristics of the whole light guide plate, such as dense and uneven distribution of light guide points, the traditional image processing and detection methods require experienced visual engineers to do a lot of feature extraction algorithm programming and expensive code maintenance. The accuracy is low and the stability is poor. However, the surface defects of the light guide plate are still mainly detected by artificial visual observation, and only a few manufacturers use traditional image processing methods to detect them. For this reason, a defect detection method based on deep learning semantic segmentation is proposed. In this method, the defect features of the light guide plate are extracted by self-learning by training the neural network, so as to avoid the complicated programming work of feature extraction algorithm. First of all, the defects of the collected light guide plate are marked to make a sample set; secondly, the pre-trained pyramid scene parsing network (PSPNet) is used to retrain the labeled samples; furthermore, the defect detection of the light guide plate is realized by using the trained model. The single deep learning semantic segmentation defect detection method usually can not meet the needs of industrial applications, finally, it is necessary to combine the simple machine vision method to judge and screen all the suspected defect regions detected by the deep learning semantic segmentation method. The experimental results show that the detection rate of bright spots, dark spots and scratches is as high as 96%, which can basically meet the requirements of industrial inspection.