Due to various complex environmental factors and parking scenes, there are more stringent requirements for automatic parking than the manual one. The existing auto-parking technology is based on space or plane dimension, where the former usually ignores the ground parking spot lines which may cause parking at a wrong position, while the latter often costs a lot of time in object classification which may decreases the algorithm applicability. In this paper, we propose a Generative Parking Spot Detection algorithm which uses a multi-clue recovery model to reconstruct parking spots. In the proposed method, we firstly dismantle the parking spot geometrically for marking the location of its corresponding corners and then use a micro-target recognition network to find corners from the ground image taken by car cameras. After these, we use the multi-clue model to correct the fully pairing map so that the reliable true parking spot can be recovered correctly. The proposed algorithm is compared with several existing algorithms, and the experimental result shows that it has a higher accuracy than others which can reach more than 80% in most test cases.

由于各种复杂的环境因素和停车场景，自动停车比手动停车有更严格的要求。现有的自动泊车技术是基于空间或平面维度的，前者通常忽略地面停车点线，可能会导致停在错误的位置，而后者往往在物体分类上花费大量时间，可能会降低算法适用性。在本文中，我们提出了一种生成停车位检测算法，该算法使用多线索恢复模型来重建停车位。在所提出的方法中，我们首先以几何方式拆除停车位以标记其相应角落的位置，然后使用微目标识别网络从车载摄像头拍摄的地面图像中找到角落。在这些之后，我们使用多线索模型对完全配对的地图进行校正，从而可以正确恢复可靠的真实停车位。将提出的算法与现有的几种算法进行对比，实验结果表明，该算法的准确率高于其他算法，在大多数测试用例中可以达到80%以上。