PSDet: Efficient and Universal Parking Slot Detection
This work addresses a critical need for robust parking slot detection in autonomous valet parking, though it is incremental as it builds on existing methods with new data and architectural improvements.
The paper tackles the problem of low generalization in real-time parking slot detection for valet parking systems by introducing a large-scale annotated benchmark and a novel method using circular descriptors and a two-stage deep architecture, achieving state-of-the-art accuracy while maintaining real-time performance.
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