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Increasing Trust in Data-Driven Model Validation

A Framework for Probabilistic Augmentation of Images and Meta-data Generation Using Application Scope Characteristics
 
: Jöckel, Lisa; Kläs, Michael

:

Romanovsky, A.:
Computer safety, reliability, and security. 38th International Conference, SAFECOMP 2019. Proceedings : 11-13 September 2019, Turku, Finand
Cham: Springer, 2019 (Lecture Notes in Computer Science 11698)
ISBN: 978-3-030-26600-4
ISBN: 3-030-26600-1
ISBN: 978-3-030-26601-1
S.155-164
International Conference on Computer Safety, Reliability, and Security (SAFECOMP) <38, 2019, Turku>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
01IS16043E; CrESt
Englisch
Konferenzbeitrag
Fraunhofer IESE ()
Safety ; Traffic sign recognition; Data augmentation; Data quality; Application scope characteristics; Uncertainty; Convolutional Neural Networks

Abstract
In recent years, interest in autonomous systems has increased. To observe their environment and interact with it, such systems need to process sensor data including camera images. State-of-the-art methods for object recognition and image segmentation rely on complex data-driven models such as convolutional neural networks. Although no final answer exists yet on how to perform safety evaluation of systems containing such models, such evaluation should comprise at least validation with realistic input data, including settings with suboptimal data quality. Because many test datasets still lack a sufficient number of representative quality deficits, we consider augmenting existing data with quality deficits as necessary. For this purpose, a novel tool framework is presented and illustrated using traffic sign recognition as a use case. The extendable approach distinguishes between augmentation at the object, context, and sensor levels. To provide realistic augmentation and meta-data for existing image datasets, known context information and conditional probabilities are processed. First applications on the GTSRB dataset show promising results. The augmentation of datasets facilitates a more rigorous investigation of how various quality deficits affect the accuracy of a model in its target application scope.

: http://publica.fraunhofer.de/dokumente/N-565129.html