A Novel Vehicle Detection Framework Based on Parallel Vision

Autonomous driving has become a prevalent research topic in recent years, arousing the attention of many academic universities and commercial companies. As human drivers rely on visual information to discern road conditions and make driving decisions, autonomous driving calls for vision systems such as vehicle detection models. These vision models require a large amount of labeled data while collecting and annotating the real traffic data are time-consuming and costly. Therefore, we present a novel vehicle detection framework based on the parallel vision to tackle the above issue, using the specially designed virtual data to help train the vehicle detection model. We also propose a method to construct large-scale artificial scenes and generate the virtual data for the vision-based autonomous driving schemes. Experimental results verify the effectiveness of our proposed framework, demonstrating that the combination of virtual and real data has better performance for training the vehicle detection model than the only use of real data.