Abstract
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.
Highlights
Autonomous driving technology facilitates the driver and improves the safety of the traffic environment
This paper proposes a long and short focal length camera fusion ranging method, which firstly matches the target vehicle and license plate detected through the long focal length and short focal length cameras, calculates the vehicle width through the license plate information, and calculates the distance between the two vehicles using a pinhole model
The long and short focal length camera fusion ranging method proposed in this paper is based entirely on vehicle and license plate detection results, and the error is mainly derived from the inaccurate detection boxes, including the side vehicle detection box width beyond the actual vehicle width, distortion of side license plate width and the detection box of the distant vehicle width does not change significantly
Summary
Autonomous driving technology facilitates the driver and improves the safety of the traffic environment. The distance estimation method based on monocular vision mainly uses the camera pinhole model or inverse perspective mapping (IMP). In order to solve the above problem that the deep learning target detection network is difficult to deploy on the embedded platform and the accuracy and robustness of vehicle ranging methods are unstable caused by lacks of the actual length reference, this paper proposes a vehicle and license plate detection model based on Lightweight YOLO and the long and short focal length cameras fusion ranging method. The fusion ranging method based on matching vehicles captured by long and short focal length cameras is proposed to solve the problem that the vehicle and license plate are challenging to detect when far away
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