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.

Evaluation of Domain Randomization Techniques for Transfer Learning To address the challenge of resourceintensive data collection from real robotic environments, many deep learning applications use synthetic data to train their networks. This creates new problems when transferring the obtained knowledge from the simulated to the real world domain. Various aspects of the simulation, which do not influence the learning objective, can be randomized to enhance generalization to new domains. In this paper, we analyze the effect of these domain randomization techniques. To get an insight into their benefits, we apply them while training a grasp success classifier based on stateoftheart CNN for an industrial robot as a showcase. We generated a large synthetic data set containing 1.44M RGB images with 48 permutations of 6 different randomizations and a base scenario as training data. The resulting networks, each trained on a different subset of this data set, are evaluated on 3k real world images of the robot performing grasps. We observed the effectiveness of randomization of perspective, distractors, lighting and the grasped box. Notably, we show that pretrained networks benefit from these techniques in particular.