Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation

Hagn, Korbinian; Grau, Oliver

doi:10.1007/978-3-031-01233-4_4

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…The field of augmenting datasets with purely synthetic images, including related work, is addressed in Chapter "Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation" [HG22], where a novel approach to apply realistic sensor artifacts to given synthetic data is proposed. The better overall quality is demonstrated via established per-image metrics and a domain distance measure comparing entire datasets.…”

Section: Augmentationmentioning

confidence: 99%

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

Houben

Abrecht

Akila

et al. 2022

Deep Neural Networks and Data for Automated Driving

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Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly.

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Section: Augmentationmentioning

confidence: 99%

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

Houben

Abrecht

Akila

et al. 2022

Deep Neural Networks and Data for Automated Driving

View full text Add to dashboard Cite

show abstract

“…Furthermore, industries such as manufacturing and industrial automation benefit from synthetic sensor data for optimizing production processes. Simulated sensor data helps in assessing the performance of industrial robots, quality control systems, and predictive maintenance solutions, leading to increased efficiency and cost savings [20], [21]. Moreover, synthetic sensor data plays a role in healthcare applications, particularly in the development of wearable devices and medical sensors.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Sensor Measurement Generation With Noise Learning and Multi-Modal Information

Romanelli,

Martinelli

2023

IEEE Access

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Deep learning has transformed data generation, particularly in creating synthetic sensor data. This capability is invaluable in fields like autonomous driving, robotics, and computer science. To achieve this, we train models using real data, enabling them to replicate sensor data closely. These models introduce variations and noise, enhancing diversity and realism. Prominent techniques, including generative adversarial networks (GANs), variational autoencoders (VAEs), and recurrent neural networks (RNNs), excel in generating synthetic sensor data. Our paper focuses on Autoregressive Convolutional Recurrent Neural Networks (CRNN) for Multivariate Time Series Prediction. We incorporate Denoising Autoencoders (DAE) to mimic real-world noise characteristics. Our model is trained and validated using Ultra Wide Band (UWB) and Ultra High-Frequency Radio Frequency Identification (UHF-RFID) sensor data. It integrates sensor measurements and diverse information sources to produce synthetic data complementing realworld data. While demonstrated with UHF-RFID and UWB sensors, these techniques extend to industrial automation, healthcare and environmental monitoring. While our methodology exhibits broad potential, we present practical demonstrations with UHF-RFID and UWB sensors. Our deep neural network model allows researchers to construct datasets for algorithm validation, eliminating the need for costly and time-consuming data collection.

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“…5, is more realistic compared to manually crafted 3D scenes. Along with our sensor simulation (results discussed in the chapter 'Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation' [HG22]), we present a step to close the domain-gap between synthetic and real data. Future work will continue to analyze the influence of other factors, such as rendering fidelity, scene complexity, and composition, to further improve the capabilities of the framework and make it even more applicable for the validation of real-world AI functions.…”

Section: Publication 4 131 6 Outlook and Conclusionmentioning

confidence: 99%

Training and Validation of Visual Perception Functions for Autonomous Driving with Synthetic Data

Hagn

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This work deals with the usability of synthetically generated data for training and validation of visual perception functions applied in autonomous driving. Synthetically generated images allow the creation of safety critical scenarios which are potentially dangerous to capture in the real-world and additionally deliver pixel perfect ground truth annotations. However, applying synthetic images to perception functions trained on real-world data poses the problem of bridging the domain gap. This is true for both training and validating with synthetic images. Therefore, the domain gap has to be sufficiently understood to generate synthetically images viable to be used for training and validation. A new domain discrepancy metric is introduced and applied to optimize the parameters of a realistic sensor simulation effectively reducing the domain gap. Several influence factors on the domain gap are disentangled. The visual detection impairing factors are introduced and shown to have a high influence on the detectability of pedestrians. Additionally, these factors are used to calibrate a weighting loss function to increase the perception performance on real-world pedestrians. New methods for perception validation are introduced. The deep variational data synthesis and the classification of visual detection impairing factors. While the former method searches for perception faults by parameterized probabilistic image creation, the latter method detects perception faults by the disagreement of a detectability classifier and the actual detection result. The findings of both training and validating were influencing the creation of two synthetic validation datasets, VALERIE and SynPeDS.

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Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation

Cited by 5 publications

References 29 publications

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

Synthetic Sensor Measurement Generation With Noise Learning and Multi-Modal Information

Training and Validation of Visual Perception Functions for Autonomous Driving with Synthetic Data

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