论文标题
在哪里驾驶：一台鱼眼摄像机的自由空间检测
Where to drive: free space detection with one fisheye camera
论文作者
论文摘要
自动驾驶领域的发展与图像处理和机器学习方法领域的新发展息息相关。为了充分利用深度学习的优势，有必要拥有足够的标记培训数据。全向鱼眼摄像机尤其不是这种情况。作为解决方案，我们在本文中建议使用基于Unity3D的合成训练数据。五通算法用于创建虚拟鱼眼相机。评估了此合成训练数据，以在不同的深度学习网络体系结构中应用自由空间检测。结果表明，合成鱼眼图像可以在深度学习环境中使用。
The development in the field of autonomous driving goes hand in hand with ever new developments in the field of image processing and machine learning methods. In order to fully exploit the advantages of deep learning, it is necessary to have sufficient labeled training data available. This is especially not the case for omnidirectional fisheye cameras. As a solution, we propose in this paper to use synthetic training data based on Unity3D. A five-pass algorithm is used to create a virtual fisheye camera. This synthetic training data is evaluated for the application of free space detection for different deep learning network architectures. The results indicate that synthetic fisheye images can be used in deep learning context.
