  Systems for the automatic recognition and detection of automotive parts are
crucial in several emerging research areas in the development of intelligent
vehicles. They enable, for example, the detection and modelling of interactions
between human and the vehicle. In this paper, we quantitatively and
qualitatively explore the efficacy of deep learning architectures for the
classification and localisation of 29 interior and exterior vehicle regions on
three novel datasets. Furthermore, we experiment with joint and transfer
learning approaches across datasets and point out potential applications of our
systems. Our best network architecture achieves an F1 score of 93.67 % for
recognition, while our best localisation approach utilising state-of-the-art
backbone networks achieve a mAP of 63.01 % for detection. The MuSe-CAR-Part
dataset, which is based on a large variety of human-car interactions in videos,
the weights of the best models, and the code is publicly available to academic
parties for benchmarking and future research.
