The VALERIE tool pipeline is a synthetic data generator developed with the goal to contribute to the understanding of domain-specific factors that influence perception performance of DNNs (deep neural networks). This work was carried out under the German research project KI Absicherung in order to develop a methodology for the validation of DNNs in the context of pedestrian detection in urban environments for automated driving. The VALERIE22 dataset was generated with the VALERIE procedural tools pipeline providing a photorealistic sensor simulation rendered from automatically synthesized scenes. The dataset provides a uniquely rich set of metadata, allowing extraction of specific scene and semantic features (like pixel-accurate occlusion rates, positions in the scene and distance + angle to the camera). This enables a multitude of possible tests on the data and we hope to stimulate research on understanding performance of DNNs. Based on performance metric a comparison with several other publicly available datasets is provided, demonstrating that VALERIE22 is one of best performing synthetic datasets currently available in the open domain.

翻译：VALERIE工具流水线是一种合成数据生成器，旨在帮助理解影响深度神经网络（DNN）感知性能的领域特定因素。该工作是在德国研究项目KI Absicherung下开展的，旨在开发一种方法论，用于验证面向自动驾驶城市环境中行人检测的DNN。VALERIE22数据集通过VALERIE程序化工具流水线生成，该流水线可从自动合成的场景渲染出高真实感的传感器仿真。该数据集提供了极其丰富的元数据集合，允许提取特定的场景和语义特征（例如像素级精确的遮挡率、场景中的位置以及距摄像头的距离和角度）。这使得能够对数据进行多种可能的测试，我们希望借此推动关于理解DNN性能的研究。基于性能指标，与多个其他公开可用的数据集进行了比较，结果表明VALERIE22是目前公开领域中性能最佳的合成数据集之一。