Paper Info
Title
Knowledge Distillation for Real-time On-Road Risk Detection
Abstract
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.
Year
DOI
Venue
2020
10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00032
2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Keywords
DocType
ISBN
Knowledge Distillation,Deep Learning,Obstacle Detection,Real-time Detection
Conference
978-1-7281-6610-0
Citations 
PageRank 
References 
0
0.34
5
Authors
4
Name
Order
Citations
PageRank
Kai Su100.34
Chowdhury Md Intisar200.34
Qiangfu Zhao321462.36
Yoichi Tomioka475.54