Computer Vision problems deal with the semantic extraction of information from camera images. Especially for field crop images, the underlying problems are hard to label and even harder to learn, and the availability of high-quality training data is low. Deep neural networks do a good job of extracting the necessary models from training examples. However, they rely on an abundance of training data that is not feasible to generate or label by expert annotation. To address this challenge, we make use of the Unreal Engine to render large and complex virtual scenes. We rely on the performance of individual nodes by distributing plant simulations across nodes and both generate scenes as well as train neural networks on GPUs, restricting node communication to parallel learning.

翻译：计算机的视觉问题涉及从相机图像中提取信息的语义学提取信息。 特别是对于实地作物图像来说,根本的问题很难贴标签,甚至更难学习,而高质量的培训数据也很少。深神经网络从培训实例中提取必要的模型是件好事。然而,它们依赖大量的培训数据,而这些数据是无法用专家说明生成或贴标签的。为了应对这一挑战,我们利用不真实的引擎制造大而复杂的虚拟场景。我们依靠单个节点的性能,在节点上传播工厂模拟,同时在GPU上生成场景以及培训神经网络,将节点通信限制在平行学习上。