Real-Time Object Detection for LiDAR Based on LS-R-YOLOv4 Neural Network

Abstract

Recently, self-driving cars became a big challenge in the automobile industry. After the DARPA challenge, which introduced the design of a self-driving system that can be classified as SAR Level 3 or higher levels, driven to focus on self-driving cars more. Later on, using these introduced design models, a lot of companies started to design self-driving cars. Various sensors, such as radar, high-resolution cameras, and LiDAR are important in self-driving cars to sense the surroundings. LiDAR acts as an eye of a self-driving vehicle, by offering 64 scanning channels, 26.9° vertical field view, and a high-precision 360° horizontal field view in real-time. The LiDAR sensor can provide 360° environmental depth information with a detection range of up to 120 meters. In addition, the left and right cameras can further assist in obtaining front image information. In this way, the surrounding environment model of the self-driving car can be accurately obtained, which is convenient for the self-driving algorithm to perform route planning. It is very important for self-driving to avoid the collision. LiDAR provides both horizontal and vertical field views and helps in avoiding collision. In an online website, the dataset provides different kinds of data like point cloud data and color images which helps this data to use for object recognition. In this paper, we used two types of publicly available datasets, namely, KITTI and PASCAL VOC. Firstly, the KITTI dataset provides in-depth data knowledge for the LiDAR segmentation (LS) of objects obtained through LiDAR point clouds. The performance of object segmentation through LiDAR cloud points is used to find the region of interest (ROI) on images. And later on, we trained the network with the PASCAL VOC dataset used for object detection by the YOLOv4 neural network. To evaluate, we used the region of interest image as input to YOLOv4. By using all these technologies, we can segment and detect objects. Our algorithm ultimately constructs a LiDAR point cloud at the same time; it also detects the image in real-time.