Data Synthesis Methods for Parking-Slot Detection

Parking-slot detection plays a critical role in the self-parking system for autonomous driving. To enhance the complexity of the environmental situations in parking-slot datasets and reduce the difficulty of manual annotation, we design several data synthesis methods to generate new parking-slots under different situations. Methods introduced in this paper include synthesizing parking-slots in AVM (around view monitor) images, generating parking-slots in fisheye images and adding 2D symbols inside parking-slots to form special ones. To test the influence of our synthetic data, we conduct a series of experiments on different tasks. In the parking-slot detection experiments, we design a novel two-stage parking-slot detection method. We use YOLOv7 as the object detector and different from previous methods, we detect the complete parking-slots and marking points at the same time. Then we match marking points and give them a certain order in the second stage. We achieve accuracy of 80.28% and recall of 79.94% on our own data which shows the effectiveness of our method. Then we achieve accuracy of 80.98% and recall of 80.48% with supplementation of synthetic parking-slots data, slightly better with no extra manual annotation. Next, we achieve accuracy of 96.78% and recall of 93.68% in another parking-slot detection experiment for the new type of parking-slots with synthetic symbols inside them. To test the generalization performance of our synthetic data, we conduct semantic segmentation experiments on public dataset. MIoU (Mean Intersection over Union) and the IoU of lane markers decrease by 0.23% and 0.57% respectively under the interference of synthetic parking-slots when parking-slot lines set the same as lane markers.