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：海量图像数据集是自动驾驶系统感知能力发展的基础。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。足够规模的数据集需包含具有挑战性的场景与目标。为测试最终功能，必须能从数据集中检索并提取这些场景与目标。虽然采集大量未标注数据相对容易，但发现具有挑战性的场景与目标却困难得多。然而在感知系统开发过程中，必须无需进行冗长耗时的标注即可访问具有挑战性的数据。开发者需要能够动态搜索数据集中的特定场景与目标。为此，我们设计了一种基于前沿神经网络的方法，可对图像中具有特定属性的目标进行检索。为提升易用性，该检索通过自然语言描述查询条件。我们采用定性定量方法在自动驾驶数据集上评估了该方法，以确定其在时间节省与性能提升方面的效果。