Abstract: A parking space detection algorithm based on right-side single-channel camera is proposed in this paper for automatic parking. Firstly, a scheme of top-hat transformation combined with histogram equalization is adopted to reduce the effects of uneven illumination and varying light intensity on the robustness of algorithm. Combined with parking condition, the worthless area is ignored based on prior knowledge, the parameter range of Radon transform is compressed, and while getting the coordinates in Radon space, the dimensions of Radon transform is reduced. Then the element values in Radon matrix are optimized by using threshold segmentation for more accurately and effectively getting cluster centers in K-means clustering, and non maximum suppression is used to achieve pixel-level location of parking lanes in the 2D neighborhood of cluster centers. Finally, hierarchical finite state machine is designed, and the classification of parking space is fulfilled by setting up the classification rule base for parking space. The results of experiments show that the computational efficiency of this method is higher than that of the classical method based on Radon transform, and the algorithm has stronger robustness

Key words: automatic parking, top-hat transform combined with histogram equalization, K-means clustering, Radon transform, hierarchical finite state machine