Training Object Detectors With Noisy Data 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 coteaching, 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.

The Impact of Domain Randomization on Object Detection: A Case Study on Parametric Shapes and Synthetic Textures * Recent advances in deep learningxe2x80x93based object detection techniques have revolutionized their applicability in several fields. However, since these methods rely on unwieldy and large amounts of data, a common practice is to download models pretrained on standard datasets and finetune them for specific application domains with a small set of domainxe2x80x93relevant images. In this work, we show that using synthetic datasets that are not necessarily photorealistic can be a better alternative to simply finetune pretrained networks. Specifically, our results show an impressive 25%improvement in the mAP metric over a finetuning baseline when only about 200 labelled images are available to train. Finally, an ablation study of our results is presented to delineate the individual contribution of different components in the randomization pipeline.