Instance-level Object Detection of Grocery Store items using R-CNN:s

Currently, the best ways to perform object detection in an image is to use a neural network trained on large sets of annotated data. However, in certain environments, the number of objects is large and gathering a sufficiently big set of annotated data is prohibitively expensive. In this thesis, the possibility of using computer-generated artificial data instead in such an environment, is investigated. By training on computer generated images, the neural network may then be able to transfer this knowledge to the real world. More specifically, we investigate this method on the problem of detecting objects commonly found in grocery stores. This could then enable an efficient pipeline, in which every object would be photographed and transferred into a textured 3D-model, which would then be used to generate the annotated images to train the network. This would remove the problem of gathering annotated data. It is concluded that indeed, constitutes a possible approach to solving the problem