TY -的A2 Butun伊斯梅尔AU -风扇,Yu-Cheng盟——Yelamandala Chitra Meghala AU - Chen Ting-Wei盟——黄,Chun-Ju PY - 2021 DA - 2021/05/26 TI -激光雷达基于LS-R-YOLOv4神经网络实时目标检测SP - 5576262六世- 2021 AB -最近,无人驾驶汽车成为汽车行业的一个巨大的挑战。国防部高级研究计划局的挑战之后,介绍了设计的自动驾驶系统,可分为SAR 3级或更高的水平,推动多集中在自动驾驶汽车。后来,使用这些设计模型介绍,很多公司开始设计自动驾驶汽车。各种传感器,如雷达、高分辨率相机和激光雷达在自动驾驶汽车,可以感知周围的环境很重要。激光雷达作为无人驾驶车辆的注意,通过提供64扫描频道,26.9°垂直视场,实时高精度360°水平字段视图。激光雷达传感器可以提供360°环境深度信息的探测距离达120米。此外,左、右摄像机可以进一步协助获取图像信息。这样,无人驾驶汽车的周边环境模型可以准确地获得,这是方便自动驾驶算法执行路线规划。为自动驾驶是非常重要的,以避免碰撞。激光雷达提供了水平和垂直领域的观点和有助于避免碰撞。 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. SN - 1687-725X UR - https://doi.org/10.1155/2021/5576262 DO - 10.1155/2021/5576262 JF - Journal of Sensors PB - Hindawi KW - ER -