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Huge image data sets are the fundament for the development of the perceptionof automated driving systems. A large number of images is necessary to trainrobust neural networks that can cope with diverse situations. A sufficientlylarge data set contains challenging situations and objects. For testing theresulting functions, it is necessary that these situations and objects can befound and extracted from the data set. While it is relatively easy to record alarge amount of unlabeled data, it is far more difficult to find demandingsituations and objects. However, during the development of perception systems,it must be possible to access challenging data without having to performlengthy and time-consuming annotations. A developer must therefore be able tosearch dynamically for specific situations and objects in a data set. Thus, wedesigned a method which is based on state-of-the-art neural networks to searchfor objects with certain properties within an image. For the ease of use, thequery of this search is described using natural language. To determine the timesavings and performance gains, we evaluated our method qualitatively andquantitatively on automotive data sets.

Unveiling Objects with SOLA: An Annotation-Free Image Search on the Object Level for Automotive Data Sets