Detection of Road Slippery using Road CCTV Images based on Convolutional Neural Networks and Transfer Learning

Road slippery detection is one of the main factors in order to increase road and passenger safety, as well as the development of autonomous vehicles and related technologies. In this regard, various researches have been done with different methods and sensors, using different data such as image, sound and wave frequency. In this article, we have detecetd the road slippery without the use of expensive sensors and methods and only using CCTV images on the roads and based on convoloudnational neural networks. The main idea of this research is the use of transfer learning approach. Therefore, first, the importance and benefits of using transfer learning are expressed in the form of network training with InceptionNetv3 architecture. In the next step, a ResNet50 CNN and a recurrent neural network are combined by using a new framework called GFNet and are trained by using transfer learning. Finally, a tool with the ability to detect the road surface, in three classes of dry, wet and snow, has been obtained with an accuracy of 96.33%.