Huge image data sets are the fundament for the development of the perception of automated driving systems. A large number of images is necessary to train robust neural networks that can cope with diverse situations. A sufficiently large data set contains challenging situations and objects. For testing the resulting functions, it is necessary that these situations and objects can be found and extracted from the data set. While it is relatively easy to record a large amount of unlabeled data, it is far more difficult to find demanding situations and objects. However, during the development of perception systems, it must be possible to access challenging data without having to perform lengthy and time-consuming annotations. A developer must therefore be able to search dynamically for specific situations and objects in a data set. Thus, we designed a method which is based on state-of-the-art neural networks to search for objects with certain properties within an image. For the ease of use, the query of this search is described using natural language. To determine the time savings and performance gains, we evaluated our method qualitatively and quantitatively on automotive data sets.

翻译：海量图像数据集是开发自动驾驶系统感知功能的基础。训练能够应对多样化场景的鲁棒神经网络需要大量图像数据。足够大的数据集包含具有挑战性的场景和目标。为测试最终功能，需要能够从数据集中检索并提取这些场景和目标。虽然采集大量未标注数据相对容易，但寻找高难度场景和目标则困难得多。然而，在感知系统的开发过程中，必须能够在无需进行耗时标注的情况下访问具有挑战性的数据。因此，开发者应当能够动态搜索数据集中的特定场景和目标。为此，我们设计了一种基于当前最先进神经网络的方法，用于在图像内搜索具有特定属性的目标。为便于使用，该搜索的查询条件采用自然语言描述。为评估时间节省与性能提升效果，我们在自动驾驶数据集上进行了定性和定量评估。