Radar as a teacher: weakly supervised vehicle detection using radar labels
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 plentif...
| Main Authors: | , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2020
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| Summary: | 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. |
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