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Systems for the automatic recognition and detection of automotive parts arecrucial in several emerging research areas in the development of intelligentvehicles. They enable, for example, the detection and modelling of interactionsbetween human and the vehicle. In this paper, we quantitatively andqualitatively explore the efficacy of deep learning architectures for theclassification and localisation of 29 interior and exterior vehicle regions onthree novel datasets. Furthermore, we experiment with joint and transferlearning approaches across datasets and point out potential applications of oursystems. Our best network architecture achieves an F1 score of 93.67 % forrecognition, while our best localisation approach utilising state-of-the-artbackbone networks achieve a mAP of 63.01 % for detection. The MuSe-CAR-Partdataset, 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 academicparties for benchmarking and future research.

Domain Adaptation with Joint Learning for Generic, Optical Car Part Recognition and Detection Systems (Go-CaRD)