论文标题
PSDET：高效且通用的停车位检测
PSDet: Efficient and Universal Parking Slot Detection
论文作者
论文摘要
尽管实时停车位检测在代客停车系统中起着至关重要的作用，但现有方法在现实世界应用中取得了有限的成功。我们认为，考虑到不令人满意的性能的两个原因：\ romannumeral1，可用的数据集具有有限的多样性，这会导致低概括能力。 \ romannumeral2，停车位检测的专家知识不足。因此，我们注释了一个大规模的基准，用于培训网络并将其释放为社区的利益。在观察基准中各种停车场的驱动下，我们提出了圆形描述符，以退回停车位槽顶点的坐标，并因此准确地定位了插槽。为了进一步提高性能，我们开发了一个两阶段的深度体系结构，以粗到精细的方式定位顶点。在我们的基准和其他数据集中，它在实践中实时实现了最先进的准确性。基准可在以下网址获得：https：//github.com/wuzzh/parking-slot-dataset
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
