Vision-Based Parking Slot Detection Based on End-to-End Semantic Segmentation Training

摘要

This work presented an end-to-end training model for parking slot detection in automatic parking systems (APSs), which combined both a line and a point semantic segmentation models based on multi-task learning. The proposed models generate images of entrance line and center points of corners, and are used to determine the coordinates of actual parking slots in the post processing step. The recall, precision and F-measure rate of the proposed method are 92.94%, 99.40% and 96.06%, respectively, which are better than existing state-of-the-art methods with end-to-end training.