Utilization of the open source datasets for semantic segmentation in automotive vision Great advancements in deeplearningbased machine vision are boosting the automotive industry potential to accurately recognize vehicle environment. The need for high quality datasets is of critical importance when training neural networks which are used to detect and classify objects in front of the vehicle. Available open source datasets for semantic segmentation can be used by a wide community of researchers to develop next generation selfdriving functions. Those datasets have severe limitations such as class imbalance, unobserved objects, erroneous labelling and limited number of covered scenarios. In this paper, we propose a sequence of steps to combine and manipulate existing open source datasets to maximize the inference performance. Those steps include relabeling with outlier removal, classdriven balancing of validation and training datasets, as well as targeted image manipulation for scarce classes. Our evaluation indicates the improved inference accuracy when compared to the usage of most common open source datasets.

Using ClassDependent PostProcessing to Improve AI for Automotive Front View Camera Deep learning approaches in artificial intelligence are being increasingly applied to many modern applications, including autonomous driving. Semantic segmentation is one of the promising ways to map vehicle environment utilizing a front view camera. In this paper we propose a postprocessing classdependent method to improve accuracy and configurability of semantic segmentation, arguing that this method is appropriate for front view application. We utilized morphological transformations to overcome the effects of noise and class overlap as well as to improve visibility of classes which had limited presence in the input dataset. Using this method, the output can increase in accuracy to up to 3.88%.