TY - JOUR A2 - Diraco，乔瓦尼AU - 刘峻AU - 张，瑞PY - 2020 DA - 2020年3月20日TI - 车辆检测和测距使用两种不同的焦距相机SP - 4372847 VL - 2020 AB - 车辆检测是自主驾驶的关键任务，要求精度高，实时速度。考虑到当前的深学习对象检测模型尺寸过大而被部署在车辆上，本文介绍的轻量级的网络修改YOLOv3的特征提取层，并提高剩余卷积结构，以及改进的轻型YOLO网络降低了数网络参数四分之一。然后，车牌被检测，以计算实际的车辆宽度和车辆之间的距离由宽度估计。本文提出了基于两个不同的焦距的摄像机检测和测距融合方法来解决难以检测和精度低引起的小牌照的问题当距离很远。The experimental results show that the average precision and recall of the Lightweight YOLO trained on the self-built dataset is 4.43% and 3.54% lower than YOLOv3, respectively, but the computing speed of the network decreases 49 ms per frame. The road experiments in different scenes also show that the long and short focal length camera fusion ranging method dramatically improves the accuracy and stability of ranging. The mean error of ranging results is less than 4%, and the range of stable ranging can reach 100 m. The proposed method can realize real-time vehicle detection and ranging on the on-board embedded platform Jetson Xavier, which satisfies the requirements of automatic driving environment perception. SN - 1687-725X UR - https://doi.org/10.1155/2020/4372847 DO - 10.1155/2020/4372847 JF - Journal of Sensors PB - Hindawi KW - ER -