ParallelEye Pipeline: An Effective Method to Synthesize Images for Improving the Visual Intelligence of Intelligent Vehicles
提出ParallelEye管道框架，通过图像到图像翻译和模拟场景自动生成带多种真实标注的合成图像，实验表明合成图像能提升真实自动驾驶场景中检测器的性能。
Virtual simulated scenes are becoming a critical part of autonomous driving. In the context of knowledge automation and machine learning, simulated images are widely used for visual environmental perception. However, even the most inspirational applications have not fully exploited the potential of simulated images in solving real-world problems. In this article, we propose a novel framework “ParallelEye Pipeline,” which uses image-to-image translation and simulated images to automatically generate realistic synthetic images with multiple ground-truth annotations. Specifically, this method has three steps: first, we use Unity3D software to simulate driving scenarios and generate simulated image pairs (including raw images and six ground-truth labels) from the simulated scenes; second, advanced image-to-image translation algorithms can generate realistic and high-resolution synthetic images from simulated image pairs; third, we exploit publicly datasets, simulated images, and synthetic images to conduct experiments for visual perception. The experimental results suggest: 1) synthetic images and simulated images can improve the performance of detectors in real autonomous driving scenarios and 2) image-to-image translation algorithms can be affected by occlusion condition.