Enhancing AVM-based parking-slot detection with synthetic data

Parking-slot detection is pivotal for autonomous driving, facilitating automated parking and enhancing vehicle safety. However, the diversity of parking slot shapes and the complexity of surrounding environments incur significant costs in data collection and annotation. To address this challenge, we propose a novel data synthesis framework specifically tailored for around-view monitor (AVM) images. First, an inpainting-based generative algorithm eliminates foreground elements from real parking slot images to produce clean backgrounds. Subsequently, new foreground elements exhibiting diverse shapes, colors, and textures are superimposed onto these backgrounds. Furthermore, rather than relying on domain selection, we introduce a data selection strategy based on active learning that operates directly on the generated datasets. The controllable attributes of synthetic data facilitate the effective evaluation and optimization of the selection strategy across various scenarios. Experimental results on the panoramic surround view (PSV) dataset demonstrate that models trained exclusively with synthetic data achieve 1.32% higher precision than those trained only on real images. Moreover, integrating 40% real images with synthetic data increases precision by up to 1.74% and recall rates by up to 1.48%, highlighting the effectiveness and practical utility of our proposed approach.