Tim Dellmann

"Robust object recognition for agricultural robots through augmentation with simulated data sets"

Agricultural robots increasingly have to rely on artificial intelligence. As the tasks are getting more and more complex, like the recognition of certain attributes of plants, also the requirements for the training data gets more difficult. Robust recognition under a given daytime or specific plant disease demands a large diversity of such data, which are further only available for a limited time in a year. Mature game engines allow highly realistic modeling of environments, which gives the ability to create different light and weather conditions. A virtual robot can then collect datasets that replace or enhance existing ones. Further, the application of Generative Adversarial Networks (GANs) is possible to transform such simulated images into photo-realistic ones. A combination of both approaches is to be developed, which provides training and testing of agricultural object recognition algorithms all over the year.

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