Super Resolution Generative Adversarial Networks for Image Supervise Learning

Abstract

The E-Tilang application system has been widely used to support modern traffic, whereas protocol roads in big cities in Indonesia are already widely used. In principle, the plate number detection tool uses image recognition for detection. Image number plates on vehicles cannot always be read clearly, this is what causes the detection method to be a problem if the image plate number is further processed. The method for processing the plate number image uses deep learning and computer vision methods. For the condition of the image plate number that is not clear, the process of improving the image resolution from low resolution to high resolution is carried out, by applying Generative Adversarial Networks. This method consists of two main parts, namely Generate and Discriminator. Generate serves to generate an image and the Discriminator here is to check the image, can the image plate number be read or not? So that if the image plate number cannot be read, then the process is carried out again to the Generator until it is received by the Discriminator to be read. The process does not end here, the results will be carried out in the next process using Convolutional Neural Networks. Where the process is to detect the plate number image according to the classification of the plate number according to the region. The point is that an unclear image becomes clear by increasing the resolution from low resolution to high resolution so that it is easily read by the Convolutional Neural Network (CNN) algorithm so that the image is easily recognized by the CNN Algorithm. This becomes important in the CNN algorithm process because it gets the processed dataset. To produce a good model, preprocessing of the dataset is carried out. So that the model can detect the image well in terms of model performance.