A RGB-D Based Real-Time Multiple Object Detection and Ranging System for Autonomous Driving

Abstract

Real-time object detection and ranging of multiple objects on the road are the essential tasks in the field of autonomous driving. In this paper, we introduce a system for simultaneous detection and ranging of vehicles, people, non-motor vehicles and lanes based on RGB-D images. Among them, the detection of vehicles, people and non-motor vehicles belongs to general detection task and the lane detection belongs to segmentation task. In order to improve the accuracy and speed, we use two networks to complete these two tasks. We propose a real-time synchronization method with multi-GPU, which achieves separate training and simultaneous detection of lane detectior and vehicle, people and non-motor vehicle detector. We also propose a center-selective ranging module based on binocular ranging technology to distance the detected object. The system reaches nearly 15 FPS with four 1080Ti GPUs. We construct datasets about these problems including daytime and night in which the system achieves high accuracy. A real-time test of the system on the streets of Tianjin, China has been conducted by us, it has proved that the system can be applied to actual driving.