In the vehicle panoramic system, how to accurately detect the position and direction of parking slot is still a problem to be solved. To solve this problem, we designed a two-way parallel multi-scale mark point detection network. The two-way network is used to detect the position and angle of mark point respectively. Multi-scale features are extracted from panoramic images, and a branch network of one high and one low resolution is maintained in parallel. The two branches are fused with each other. The high-resolution features express the location of mark point in the form of Gaussian heatmap. A new method for calculating the direction of parking slots is proposed, which uses the direction of two mark points and the relative position of two mark points to calculate the direction of parking slot. In order to verify the feasibility of the proposed method, the designed network was trained using the training set of the public dataset PS2. 0, and the parking slot detection precision tested on the public dataset PS2. 0 and the self-collected dataset PSS is 99. 4% and 95. 27%, the recall is 99. 88% and 80. 89%, the average error of the mark point position on PS2. 0 is 0. 84 pixel, and the error of the parking direction is 0. 71 degree. The experimental results show that compared with the existing methods, the parking slot detection network proposed reduces the errors in the location of the mark point and the direction of the parking slot, and has a strong generalization ability on the PSS dataset.