Camera Vignetting Model and its Effects on Deep Neural Networks for Object Detection In this paper, we describe a new approach for synthetic image augmentation and its advantages in training Deep Neural Networks (DNNs) for object classification and localization. To address the need for a significant amount of data when training DNNs, for imagebased ADAS functions, our method relies on virtually generated scenarios augmented via a physicsbased camera model. The camera model implements various optical effects on idealsynthetic images. For the scope of this paper, we illustrate the performance differences associated with the vignetting effect when training DNNs with and without image augmentation. We show that training on images altered by our camera vignetting model yield to a better performance than using idealsynthetic images, additionally we illustrate the relationship between the networku0027s performance results and the implemented effect (vignetting in this case). For a start, our results open the possibility for using camera models for training neural networks on synthetic data and pave the way toward further investigations on significant optical and image sensor effects to be modeledimplemented for performance enhancement during the training process. The approach is conducted and evaluated by training a DNN for car detection using the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI) and Virtual KITTI (VKITTI) datasets.

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.