T-psd: T-shape parking slot detection with self-calibrated convolution network

Abstract

This paper deals with a challenging autonomous parking problem in which the parking slots are with various different angles. We transform the problem of parking slot detection into center keypoint detection, representing the parking slot as a T-shape to make it robust and simple. For diverse types of parking slots, we propose a T-shape parking slot detection method, called T-PSD, to extract the T-shape center information based on a self-calibrated convolution network (SCCN). This method can concurrently obtain the entrance center confidence, the relative offsets of the paired junctions, the direction of the middle line, the occupancy and the inferred type in the parking slots. Final detection results are produced by utilizing Half-Heatmap, MultiBins and Midline-Grid to more accurately extract the center keypoint, direction and occupancy, respectively. To verify the performance of our method, we conduct experiments on the public PS2.0 dataset. The results have shown that our method outperforms state-of-the-art competitors by showing recall rate of 99.86% and precision rate of 99.82%. It is capable of achieving 65 frames per second (FPS) and satisfying a real-time detection performance. In contrast to the simultaneous detection of global and local information, our SCCN detector exclusively concentrates on the T-shape center information, which achieves comparable performance and significantly accelerates the inference time without non-maximum suppression (NMS).