Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICRA40945.2020.9196855 | 2020 IEEE International Conference on Robotics and Automation (ICRA) |
Keywords | DocType | Volume |
noisy labels,noise-aware training techniques,training data,weakly supervised vehicle detection,radar labels,object detector,image-based vehicle detection | Conference | 2020 |
Issue | ISSN | ISBN |
1 | 1050-4729 | 978-1-7281-7396-2 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Chadwick | 1 | 0 | 0.68 |
| Paul Newman | 2 | 4364 | 321.76 |