Weather Data Integrated Mask RCNN for Automatic Road Surface Condition Monitoring Monitoring road surface conditions plays a crucial role in driving safety and road maintenance, especially in winter seasons. Traditional methodologies often employ manual inspection and expensive instruments, e.g., NIR cameras. However, image analysis based on normal cameras can provide an economical and efficient solution for road surface monitoring. This paper presents an automatic classification model of road surface conditions using a deep learning approach based on road images and weather measurement. A modified mask RCNN model has been developed by integrating weather data based on transfer learning. Experimental results with respect to manual judgment of road surface conditions have demonstrated very high accuracy of the developed model.

Weather Classification on Roads for Drivers Assistance using Deep Transferred Features Extreme weather conditions such as heavy rain, fog and scorching heat of sun increase the risk factor of road accidents and traffic congestion. This impels valuable lives and property into tremendous dangers and causes dilatory. Therefore, automatic weather detection on roads for the assistance of drivers becomes very crucial. Weather detection and forecasting is usually figured out by using temperature, humidity and wind sensors. In this paper, we introduce a methodology which exploits visual data to detect the weather condition. We propose a weather detection system based on stateoftheart deep learning techniques by transferring the learned weights of pretrained inception v3 model to our problem. Proposed system can be utilized to generate alerts for upcoming vehicleu0027s drivers to change their driving behavior according to the weather condition. The system can also be helpful for Intelligent Transportation System (ITS) authorities so that they can provide road safety to the citizens with efficient way and improve the current ITS. The system is trained using selfgenerated dataset. Proposed system achieved an accuracy of 98% on unseen dataset, which is very high as compare to prior models.