A new architecture based on convolutional neural networks (CNN) for assisting the driver in fog environment

Driver Assistance Systems (ADAS) are designed to assist the driver and improve road safety. For this, various sensors are generally embedded in vehicles to alert the driver in case of danger present on the road. Unfortunately, the performance of such systems degrades in the presence of adverse weather conditions. In addition, eliminating the fog of a single image captured by a camera is a very difficult and ill-posed phenomenon in Advanced Driver Assistance Systems (ADAS). Recent developments in the field of deep learning have allowed researchers to build relevant models using various tools available. We propose in this paper a new architecture based on fast R-CNN for the detection of objects in fogged images, and a convolutional neuron network (CNN) is designed on the basis of a reformulated model of atmospheric diffusion for fog elimination to restore the sharp image.