Knowledge Distillation for Real-time On-Road Risk Detection

On-road risk detection is one of the most important applications of Deep Neural Network (DNN). A good on-road risk detection system can accurately detect dangerous obstacles on the road and provides drivers with early warning information to avoid danger. However, current high-precision detection systems usually use a cumbersome DNN, which require a large number of computing resources. Due to the limitations of mobile devices such as limited computing power and power consumption problem, it is difficult to apply the high-precision detection model to such devices for running real-time applications. Therefore a fast and accurate model is needed to solve the on-road risk detection problem for low-cost mobile devices. In this paper, we propose a real-time on-road risk detection framework for the low computational platform. The proposed framework uses a tiny neural network model that improves accuracy by leveraging the knowledge distillation technique. By using the proposed method, we conduct our experiments in a Raspberry Pi using one Intel Neural Compute Stick-2 (NCS-2). The practical results show that our detection system can successfully detect the position and type of road obstacles in real-time while maintaining performance comparable to more complex DNN.