09:30 - 09:45 | Mon 1 Jun | Room T6 | MoA06.2
It has been demonstrated that the performance of an object detector degrades when it is used outside the domain of the data used to train it. However, obtaining training data for a new domain can be time consuming and expensive. In this work we demonstrate how a radar can be used to generate plentiful (but noisy) training data for image-based vehicle detection. We then show that the performance of a detector trained using the noisy labels can be considerably improved through a combination of noise-aware training techniques and relabelling of the training data using a second viewpoint. In our experiments, using our proposed process improves average precision by more than 17 percentage points when training from scratch and 10 percentage points when fine-tuning a pre-trained model.
