Real-time Object Detection Based on RGB-D Image

Object detection for environment perception is one of the most critical steps for autonomous driving and driving assistant systems. Currently, considering the size of detection model, there is still a need for further improvement to satisfy the requirement of real-time, high-accuracy in object detection approaches for embedded on-board devices. In this paper, we proposed a novel method that taking advantage of fully convolutional neural network and RGB-D image to enhance the speed of detection and simplify the model by reducing its size. Specifically, we utilized a simulator to obtain a lot of binocular and supplementary RGB-D images for training. The proposed method was evaluated on the KITTI dataset and can achieve an accuracy of 90% for easy task. Moreover, the simulation results demonstrated the efficiency of detection get a speed up to 180 FPS when running on the GPU.