  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.
