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For high resolution scene mapping and object recognition, opticaltechnologies such as cameras and LiDAR are the sensors of choice. However, forrobust future vehicle autonomy and driver assistance in adverse weatherconditions, improvements in automotive radar technology, and the development ofalgorithms and machine learning for robust mapping and recognition areessential. In this paper, we describe a methodology based on deep neuralnetworks to recognise objects in 300GHz radar images, investigating robustnessto changes in range, orientation and different receivers in a laboratoryenvironment. As the training data is limited, we have also investigated theeffects of transfer learning. As a necessary first step before road trials, wehave also considered detection and classification in multiple object scenes.

300 GHz Radar Object Recognition based on Deep Neural Networks and Transfer Learning