Training Object Detectors With Noisy Data
This addresses the challenge of reducing labeling costs for object detection, but it is incremental as it adapts an existing method to a new domain.
The paper tackles the problem of training object detectors with noisy labels, which arise from automatic labeling methods, by adapting and improving the co-teaching method to mitigate noise effects, demonstrating results on simulated noise in KITTI and a vehicle detection task.
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.