Abstract
The self-parking system plays an important role in autonomous driving, and one of its critical issues is parking-slot detection. Previous studies in this field are mostly based on off-the-shelf models designed for universal purposes, which have various limitations in solving specific problems. In this paper, we propose a parking-slot detection method using directional marking-point regression, namely DMPR-PS. Instead of utilizing multiple off-the-shelf models, DMPR-PS uses a novel CNN-based model specially designed for directional marking-point regression. Given a surround-view image I, the model predicts position, shape and orientation of each marking-point on I. From marking-points, parking-slots on I could be easily inferred using geometric rules. DMPR-PS outperforms state-of-the-art competitors on the benchmark dataset with a precision rate of 99.42% and a recall rate of 99.37%, while achieving a real-time detection speed of 12ms per frame on Nvidia Titan Xp. To make the results reproducible, the source code is available at https://github.com/Teoge/DMPR-PS.