PSDet: Efficient and Universal Parking Slot Detection

TL;DR

This paper introduces PSDet, a real-time parking slot detection method that leverages a large-scale diverse dataset and a novel circular descriptor within a two-stage deep architecture to achieve state-of-the-art accuracy.

Contribution

The paper provides a large-scale parking slot dataset and a novel circular descriptor, enhancing generalization and accuracy in parking slot detection.

Findings

Achieves state-of-the-art accuracy on benchmark datasets.

Operates in real-time for practical applications.

Outperforms existing methods in diverse parking lot scenarios.

Abstract

While real-time parking slot detection plays a critical role in valet parking systems, existing methods have limited success in real-world applications. We argue two reasons accounting for the unsatisfactory performance: \romannumeral1, The available datasets have limited diversity, which causes the low generalization ability. \romannumeral2, Expert knowledge for parking slot detection is under-estimated. Thus, we annotate a large-scale benchmark for training the network and release it for the benefit of community. Driven by the observation of various parking lots in our benchmark, we propose the circular descriptor to regress the coordinates of parking slot vertexes and accordingly localize slots accurately. To further boost the performance, we develop a two-stage deep architecture to localize vertexes in the coarse-to-fine manner. In our benchmark and other datasets, it achieves the state-of-the-art accuracy while being real-time in practice. Benchmark is available at: https://github.com/wuzzh/Parking-slot-dataset