Mr. Frank P. A. Benders Profile

Frank P. A. Benders

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Proceedings Article | 17 October 2023 Presentation + Paper

Predicting DNN classification performance on degraded imagery

Lotte Nijskens, Richard J. den Hollander, Jorge G. Melo, Frank P. Benders, Klamer Schutte

Proceedings Volume 12742, 127420S (2023) https://doi.org/10.1117/12.2682052

KEYWORDS: Performance modeling, Image classification, Image resolution, Neural networks, Network architectures, Motion models, Image quality, Visual process modeling

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The use of deep neural networks (DNNs) is the dominant approach for image classification, as it achieves state-of-the-art performance when sufficiently large training datasets are available. The best DNN performance is reached when test data conditions are similar to the training data conditions. However, if the test conditions differ, there will usually be a loss of classification performance, for instance when the test targets are more distant, blurry or occluded than those observed in the training data. It is desirable to have an estimate of the expected classification performance prior to using a DNN in practice. A low expected performance may deem the DNN unsuitable for the operational task at hand. While the effect on classification performance of a single changed test condition has been investigated before, this paper studies the combined effect of multiple changed test conditions. In particular, we will compare two prediction models for the estimation of the expected performance compared to the DNN performance on the development data. Our approach allows performance estimation in operation based on knowledge of the expected operational conditions, but without having access to operational data itself. We investigate the aforementioned steps for image classification on the MARVEL vessel dataset and the Stanford Cars dataset. The changing test conditions consist of several common image degradations that are imposed on the original images. We find that the prediction models produce acceptable results in case of small degradations, and when degradations show a constant accuracy falloff over their range.

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