Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?

Johnson-Roberson, Matthew; Barto, Charles; Mehta, Rounak; Sridhar, Sharath Nittur; Rosaen, Karl; Vasudevan, Ram

doi:10.1109/icra.2017.7989092

Cited by 437 publications

(309 citation statements)

References 18 publications

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“…We consider this approach appealing because: (1) It enables the use powerful machine learning techniques even if no real data is available. (2) No tedious manual labeling is required. (3) Expert knowledge is directly exploited for the design of the probabilistic model.…”

Section: Discussionmentioning

confidence: 99%

End-to-end defect detection in automated fiber placement based on artificially generated data

Zambal

Heindl

Eitzinger

et al. 2019

Fourteenth International Conference on Quality Control by Artificial Vision

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Automated fiber placement (AFP) is an advanced manufacturing technology that increases the rate of production of composite materials. At the same time, the need for adaptable and fast inline control methods of such parts raises. Existing inspection systems make use of handcrafted filter chains and feature detectors, tuned for a specific measurement methods by domain experts. These methods hardly scale to new defects or different measurement devices. In this paper, we propose to formulate AFP defect detection as an image segmentation problem that can be solved in an end-to-end fashion using artificially generated training data. We employ a probabilistic graphical model to generate training images and annotations. We then train a deep neural network based on recent architectures designed for image segmentation. This leads to an appealing method that scales well with new defect types and measurement devices and requires little real world data for training.

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Section: Discussionmentioning

confidence: 99%

End-to-end defect detection in automated fiber placement based on artificially generated data

Zambal

Heindl

Eitzinger

et al. 2019

Fourteenth International Conference on Quality Control by Artificial Vision

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“…• SIM 10K [28] is a simulated dataset containing 10, 000 images synthesized by the Grand Theft Auto game engine. In this dataset, which simulates car driving scenes captured by a dash-cam, there are 58, 701 annotated car instances with bounding boxes.…”

Section: Baselinesmentioning

confidence: 99%

A Robust Learning Approach to Domain Adaptive Object Detection

Khodabandeh

Vahdat

Ranjbar³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

231

160

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Domain shift is unavoidable in real-world applications of object detection. For example, in self-driving cars, the target domain consists of unconstrained road environments which cannot all possibly be observed in training data. Similarly, in surveillance applications sufficiently representative training data may be lacking due to privacy regulations. In this paper, we address the domain adaptation problem from the perspective of robust learning and show that the problem may be formulated as training with noisy labels. We propose a robust object detection framework that is resilient to noise in bounding box class labels, locations and size annotations. To adapt to the domain shift, the model is trained on the target domain using a set of noisy object bounding boxes that are obtained by a detection model trained only in the source domain. We evaluate the accuracy of our approach in various source/target domain pairs and demonstrate that the model significantly improves the state-of-the-art on multiple domain adaptation scenarios on the SIM10K, Cityscapes and KITTI datasets.

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“…[CSKX15] use TORCS [WEG*00] to evaluate the proposed direct perception model for autonomous driving. Recently, researchers [RVRK16, JRBM*17, RHK17] leverage Grand Theft Auto V (GTA V) to derive autonomous driving policies, which result in comparable performance to control policies that derived from manually annotated real‐world images.…”

Section: Applications In Autonomous Drivingmentioning

confidence: 99%

A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving

Chao

et al. 2019

Computer Graphics Forum

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Virtualized traffic via various simulation models and real‐world traffic data are promising approaches to reconstruct detailed traffic flows. A variety of applications can benefit from the virtual traffic, including, but not limited to, video games, virtual reality, traffic engineering and autonomous driving. In this survey, we provide a comprehensive review on the state‐of‐the‐art techniques for traffic simulation and animation. We start with a discussion on three classes of traffic simulation models applied at different levels of detail. Then, we introduce various data‐driven animation techniques, including existing data collection methods, and the validation and evaluation of simulated traffic flows. Next, we discuss how traffic simulations can benefit the training and testing of autonomous vehicles. Finally, we discuss the current states of traffic simulation and animation and suggest future research directions.

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Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?

Cited by 437 publications

References 18 publications

End-to-end defect detection in automated fiber placement based on artificially generated data

End-to-end defect detection in automated fiber placement based on artificially generated data

A Robust Learning Approach to Domain Adaptive Object Detection

A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving

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