Most of the existing parking spot detection solutions simply combine the target detection scheme and with manually designed post-processing modules， and there is a large amount of redundant information in the features extracted at each stage. Moreover， the manually designed post-processing modules are usually narrowly adapted and computationally intensive， which ultimately makes the parking space detection effect difficult to be practical. To address these problems， this paper introduces panoramic vision and combines the advantages of existing algorithms with the characteristics of surround-view images to design an end-to-end anchorless frame parking spot detection algorithm. The algorithm models the entry line orientation of parking spaces instead of considering two entry points separately， eliminating the process of parking space entry point matching and orientation judgment， and finally realizing realizes fully integrated parking space location， orientation， and occupancy detection. Considering the practicality， the network structure design is optimized in many aspects， such as the balance of speed and accuracy， positive and negative sample balance， and no post-processing. Finally， on the ps2.0 dataset， the AFPSD model proposed in this paper achieves 68.7% AP with a FPS（Frames Per Secend） of 88.7， which is 1.2% and 2.1% higher accuracy compared to the VPS-Net and DMPR-PS schemes， respectively. It can be seen that the one-stage end-to-end scheme designed in this paper can replace the three-stage scheme to achieve stable detection of parking slots on the surround-view image.