Current parking slot detection in advanced driver-assistance systems (ADAS) primarily relies on ultrasonic sensors. This method has several limitations such as the need to scan the entire parking slot before detecting it, the incapacity of detecting multiple slots in a row, and the difficulty of classifying them. Due to the complex visual environment, vehicles are equipped with surround view camera systems to detect vacant parking slots. Previous research works in this field mostly use image-domain models to solve the problem. These two-stage approaches separate the 2D detection and 3D pose estimation steps using camera calibration. In this paper, we propose one-step Holistic Parking Slot Network (HPS-Net), a tailor-made adaptation of the You Only Look Once (YOLO)v4 algorithm. This camera-based approach directly outputs the four vertex coordinates of the parking slot in topview domain, instead of a bounding box in raw camera images. Several visible points and shapes can be proposed from different angles. A novel regression loss function named polygon-corner Generalized Intersection over Union (GIoU) for polygon vertex position optimization is also proposed to manage the slot orientation and to distinguish the entrance line. Experiments show that HPS-Net can detect various vacant parking slots with a F1-score of 0.92 on our internal Valeo Parking Slots Dataset (VPSD) and 0.99 on the public dataset PS2.0. It provides a satisfying generalization and robustness in various parking scenarios, such as indoor (F1: 0.86) or paved ground (F1: 0.91). Moreover, it achieves a real-time detection speed of 17 FPS on Nvidia Drive AGX Xavier. A demo video can be found at https://streamable.com/75j7sj.

翻译：当前先进驾驶辅助系统（ADAS）中的停车位检测主要依赖超声波传感器。该方法存在若干局限性，例如需扫描整个停车位后方可检测、无法连续检测多个停车位以及分类困难等问题。由于复杂的视觉环境，车辆需配备环视摄像头系统以检测空闲停车位。此前该领域的研究多采用图像域模型解决此问题，这类两阶段方法通过相机标定分离二维检测与三维位姿估计步骤。本文提出单步全局停车位检测网络（HPS-Net），是基于YOLOv4算法的定制化改进方案。该基于摄像头的方法可直接输出俯视域中停车位的四个顶点坐标，而非原始摄像头图像中的边界框，能从不同角度提出多个可见点与形状。我们还提出了一种用于多边形顶点位置优化的新型回归损失函数——多边形角广义交并比（GIoU），以处理停车位朝向并区分入口线。实验表明，HPS-Net在内部Valeo停车位数据集（VPSD）上实现F1分数0.92，在公开数据集PS2.0上达到0.99，可检测各类空闲停车位。该方法在室内（F1：0.86）与铺装地面（F1：0.91）等不同停车场景中均展现出良好的泛化性与鲁棒性，并在Nvidia Drive AGX Xavier平台上达到17 FPS的实时检测速度。演示视频见https://streamable.com/75j7sj。