Design And Implementation Of Road Image Visibility Recognition System Based On Deep Learning

Road visibility judgment is of great significance to driving safety.However,the high cost of existing visibility measurement equipment make it difficult to deploy them on all sections of the expressway.The image-based visibility recognition system can make full use of the existing road monitoring equipment to realize the visibility prediction of the whole-road network,so it has great research significance and application value.Based on the background above,more specific work includes:1.To solve the lack of judgment criteria for image visibility,a set of image-visibility-grade division methods was designed,a small-scale image-visibility-grade data set was constructed according to the specification.2.Two image-visibility calculation methods based on dark channel and image-visibility recognition methods based on convolutional neural network are proposed.The former estimates transmittance of light in scene by analyzing the dark channel of the image,and then predicts the visibility of the scene;the latter implements final image visibility recognition by modeling the visibility judgment process of professional forecasters.3.A set of image-based visibility classification system are designed and implemented.The system allows users to:1)expand data sets through graphical interface,2)set multiple combinations of model parameters and perform parallel training at the same time,3)deploy the selected models rapidly by just clicking them.Experimental results show that this system can effectively predict visibility level of the scenes captured by images.