Impacts of Data Anonymization on Semantic Segmentation

For the development of machine learning-based driver assistance systems and highly automated driving functions, training data play a significant role in ensuring machine learning algorithms generalize well on real driving scenarios. However, data protection regulations in Europe require that individuals’ data should be processed in such a way that the individual cannot be identified from the collected data. Therefore, before camera images taken from test vehicles save on a server, license plates and faces of individuals should be anonymized first. Nevertheless, the impact of using anonymized data on the performance of machine learning algorithms remains unclear. Our work aims to evaluate the impact of anonymization on the task of semantic segmentation using diverse neural network architectures, a range of input image resolutions, and different anonymization patterns. We observe statistically significant effects of anonymizing image data on model performance and investigate methods for mitigating segmentation precision loss.