Name
Abstract | ||
|---|---|---|
In this paper, we discuss the relevance of training data on modern object detectors used on onboard applications. Whereas modern deep learning techniques require large amounts of data, datasets with typical scenarios for autonomous vehicles are scarce and have a reduced number of samples. We conduct a comprehensive set of experiments to understand the effect of using a combination of two relatively small datasets to train an end-to-end object detector, based on the popular Faster R-CNN and enhanced with orientation estimation capabilities. We also test the adequacy of training models using partially available ground-truth labels, as a consequence of combining datasets aimed at different applications. Data augmentation is also introduced into the training pipeline. Results show a significant performance improvement in our exemplary case as a result of the higher variability of the training samples, thus opening a new way to improve the detection performance independently from the detector architecture. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICVES.2018.8519510 | 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES) |
Keywords | Field | DocType |
road user detection,onboard applications,autonomous vehicles,end-to-end object detector,orientation estimation capabilities,training models,data augmentation,training pipeline,detection performance,detector architecture,deep learning techniques,R-CNN,ground-truth labels,object detectors | Training set,Computer vision,Object detection,Architecture,Task analysis,Feature extraction,Artificial intelligence,Deep learning,Engineering,Detector,Machine learning,Performance improvement | Conference |
ISBN | Citations | PageRank |
978-1-5386-3544-5 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Guindel | 1 | 8 | 1.87 |
| David Martín | 2 | 85 | 13.85 |
| José María Armingol | 3 | 213 | 24.74 |
| Christoph Stiller | 4 | 2189 | 153.23 |