Ihr Startpunkt für Innovation

Hyperspectral Deep Learning for Fruit and Vegetable Recognition and Bayesian Deep Learning to Accurately Determine Model Uncertainty

Konstantin Posch CTR / AAU

Abstract: Reliable automatic classification of fruits and vegetables is of great interest for a wide and ever increasing range of applications. However, due to the similarity of the classes in both shape and color the task is considered as difficult and even state of the art deep neural networks are often still not accurate enough. It will be shown how the increased spectral resolution of hyperspectral cameras in comparison to classical RGB cameras can be used to train more accurate models. Deep learning has two major drawbacks. On the one hand, deep neural networks require a huge amount of training data, otherwise they tend to over fit, on the other hand only point estimates are computed. The absence of model uncertainty information through merely computing point estimates makes deep learning of limited use for many fields of application, such as medicine. Both problems are well addressed by using Bayesian statistics. A new approach for training deep nets in a Bayesian way will be presented.

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