A color vision approach considering weather conditions based on autoencoder techniques using deep neural networks

Abstract

Color machine vision is a riveting technology crucial in pioneering innovations like autonomous vehicles, autonomous drone deliveries, automated stores, robots, infrastructure and surveillance monitoring programs for security, manufacturing defect monitoring and more. When it comes to real life applications of automated machines, safety is a major concern and to ensure utmost safety the unpredictable has to be taken into consideration. We propose and demonstrate a color vision approach that allows image normalization hinged on autoencoder techniques employing deep neural networks. The model is composed of image preprocessing, encoding and decoding. The images are resized in preprocessing portion the images go through a cognitive operation where the input image becomes suitable to enter the autoencoding technique section. The autoencoder is comprised of two core components - encoder and decoder. To employ this system deep neural network is applied which generates a code of an image in the encoding process. Sequentially, the code changes over to decoding. Decoder portion decodes it and regenerates the initial image extracting it from the code of the encoder portion. It allows normalizing color images under different weather conditions such as images captured during rainy or foggy weather conditions. We devise it such that rainy and foggy images are normalized concurrently and in real time. The autoencoder is trained with numerous rainy and foggy datasets utilizing CNN. In this research we investigate the model normalizing images in two different weather conditions - rainy and foggy conditions in real time. We used SSIM and PSNR to verify the accuracy of the model and confirm its capability reconstructing images in real time for advanced real life color vision implementations.