A novel vehicle line-pressing detection framework based on 3D object detection
Guida Zheng, Lin Jie, Yang Qin, Benying Tan · 2023
Vehicle line-pressing detection is of great significance in traffic safety, which is conducive to smooth traffic and avoiding traffic accidents. Currently, most vehicle line-pressing detection is based on 2D object detection, which provides inferior performance on accuracy and robustness. 2D object detection has inherent limitations, such as providing only positional information of vehicles in the form of bounding boxes without accurate vehicle shape and pose information. However, this information significantly impacts the effect of vehicle line-pressing detection. Therefore, this paper introduces 3D object detection technology into the area of vehicle line-pressing detection to enhance the precision of line-pressing detection. 3D object detection can provide more accurate and rich vehicle information such as vehicle shape and pose, and based on this information, a higher precision detection of line-pressing can be performed. In addition, this paper adopts an intuitive and efficient line-pressing judgment strategy. It involves obtaining the mask image of vehicles through 3D object detection and performing a logical "AND" operation with the lane mask image to determine whether the vehicle is over the line. The experimental results show that compared with the method based on 2D object detection, the vehicle line-pressing detection method based on 3D object detection has better accuracy and stronger robustness.