Vehicle detection is a crucial task for autonomous driving and demands high accuracy and real-time speed. Considering that the current deep learning object detection model size is too large to be deployed on the vehicle, this paper introduces the lightweight network to modify the feature extraction layer of YOLOv3 and improve the remaining convolution structure, and the improved Lightweight YOLO network reduces the number of network parameters to a quarter. Then, the license plate is detected to calculate the actual vehicle width and the distance between the vehicles is estimated by the width. This paper proposes a detection and ranging fusion method based on two different focal length cameras to solve the problem of difficult detection and low accuracy caused by a small license plate when the distance is far away. 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.

中文翻译：

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使用两个不同焦距相机进行车辆检测和测距

车辆检测是自动驾驶的关键任务，需要高精度和实时速度。考虑到当前的深度学习目标检测模型规模太大，无法部署在车辆上，本文引入了轻量级网络来修改YOLOv3的特征提取层并改善剩余的卷积结构，而改进的轻量级YOLO网络减少了数量网络参数的四分之一。然后，检测车牌以计算实际车辆宽度，并通过该宽度估算车辆之间的距离。提出了一种基于两个不同焦距相机的检测与测距融合方法，以解决距离较远时车牌小导致的检测困难和精度低的问题。实验结果表明，在自建数据集上训练的轻量级YOLO的平均精度和召回率分别比YOLOv3低4.43％和3.54％，但网络的计算速度每帧降低49 ms。在不同场景下进行的道路实验也表明，长焦距和短焦距相机融合测距方法显着提高了测距的准确性和稳定性。测距结果的平均误差小于4％，稳定测距范围可达100 m。所提出的方法可以在车载嵌入式平台Jetson Xavier上实现实时的车辆检测和测距，满足自动驾驶环境感知的要求。分别，但网络的计算速度每帧降低49毫秒。在不同场景下进行的道路实验也表明，长焦距和短焦距相机融合测距方法显着提高了测距的准确性和稳定性。测距结果的平均误差小于4％，稳定测距范围可达100 m。所提出的方法可以在车载嵌入式平台Jetson Xavier上实现实时的车辆检测和测距，满足自动驾驶环境感知的要求。分别，但是网络的计算速度每帧降低49毫秒。在不同场景下进行的道路实验也表明，长焦距和短焦距相机融合测距方法显着提高了测距的准确性和稳定性。测距结果的平均误差小于4％，稳定测距范围可达100 m。所提出的方法可以在车载嵌入式平台Jetson Xavier上实现实时的车辆检测和测距，满足自动驾驶环境感知的要求。稳定测距范围可达100 m。所提出的方法可以在车载嵌入式平台Jetson Xavier上实现实时的车辆检测和测距，满足自动驾驶环境感知的要求。稳定范围可达100 m。所提出的方法可以在车载嵌入式平台Jetson Xavier上实现实时的车辆检测和测距，满足自动驾驶环境感知的要求。