Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (1): 7-20.doi: 10.23919/JSEE.2021.000002

• ELECTRONICS TECHNOLOGY • Previous Articles     Next Articles

A deep learning-based binocular perception system

Zhao SUN1(), Chao MA1,*(), Liang WANG2(), Ran MENG3(), Shanshan PEI1()   

  1. 1 Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China
    2 Baidu’s Intelligent Driving Group, Beijing 100193, China
    3 Beijing Smarter Eye Technology Co., Ltd., Beijing 100023, China
  • Received:2020-02-08 Online:2021-02-18 Published:2021-03-16
  • Contact: Chao MA E-mail:sun.zhao.must@gmail.com;cma@must.edu.mo;wangliang18@baidu.com;ran.meng@smartereye.com;pei.shanshan.must@gmail.com
  • About author:|SUN Zhao was born in 1989. He received his master’s degree from Tianjin University of Science & Technology, China, in 2017. He is currently pursuing his Ph.D. degree in Faculty of Information Technology, Macau University of Science and Technology, Macau, China. His research interests include computer vision, pattern recognition, machine learning and deep learning. E-mail: sun.zhao.must@gmail.com||MA Chao was born in 1975. He received his Ph.D. degree in applied mathematics from Wuhan University, China, in 2005. He is currently an associate professor in Faculty of Information Technology, Macau University of Science and Technology, Macau, China. His research interests include Diophantine approximation, fractal geometry and applied mathematics. E-mail: cma@must.edu.mo||WANG Liang was born in 1981. He received his Ph.D. degree in computer science from University of Kentucky in 2012. He served as the area chair for International Conference on 3D Vision in 2015, 2016 and 2017. He is currently with Baidu ’s Intelligent Driving Group. His research interests include computer vision, graphics, and machine learning. E-mail: wangliang18@baidu.com||MENG Ran was born in 1980. He received his master’s degree from Tianjin University of Science and Technology, China, in 2006. He is currently with Beijing Smarter Eye Technology Co., Ltd. His research interests include computer vision, pattern recognition, and machine learning. E-mail: ran.meng@smartereye.com||PEI Shanshan was born in 1993. She received her master ’s degree from Tianjin University of Science & Technology, China, in 2018. She is currently pursuing her Ph.D. degree in Faculty of Information Technology, Macau University of Science and Technology, Macau, China. Her research interests include computer vision, pattern recognition, machine learning and deep learning. E-mail: pei.shanshan.must@gmail.com
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61673381), the National Key R&D Program of China (2018AAA0103103), and the Science and Technology Development Fund (0024/2018/A1)

Abstract:

An obstacle perception system for intelligent vehicle is proposed. The proposed system combines the stereo version technique and the deep learning network model, and is applied to obstacle perception tasks in complex environment. In this paper, we provide a complete system design project, which includes the hardware parameters, software framework, algorithm principle, and optimization method. In addition, special experiments are designed to demonstrate that the performance of the proposed system meets the requirements of actual application. The experiment results show that the proposed system is valid to both standard obstacles and non-standard obstacles, and suitable for different weather and lighting conditions in complex environment. It announces that the proposed system is flexible and robust to the intelligent vehicle.

Key words: intelligent vehicle, stereo matching, deep learning, environment perception