The availability of a large quantity of labelled training data is crucial for the training of modern object detectors. Hand labelling training data is time consuming and expensive while automatic labelling methods inevitably add unwanted noise to the labels. We examine the effect of different types of label noise on the performance of an object detector. We then show how co-teaching, a method developed for handling noisy labels and previously demonstrated on a classification problem, can be improved to mitigate the effects of label noise in an object detection setting. We illustrate our results using simulated noise on the KITTI dataset and on a vehicle detection task using automatically labelled data.
Training Object Detectors With Noisy Data
2019 IEEE Intelligent Vehicles Symposium (IV) ; 1319-1325
01.06.2019
2136230 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Large-Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds
British Library Online Contents | 2014
|British Library Online Contents | 2009
|Distilling Object Detectors with Global Knowledge
DataCite | 2024
|