| Summary: | In one of the facilities at the Stena Recycling plant in Halmstad, Sweden, about 300 tonnes of metallic waste is processed each day with the aim of sorting out all non-ferrous material. At the end of this process, non-ferrous materials are manually sorted out from the ferrous materials. This thesis investigates a computer vision based approach to identify and localize the non-ferrous materials and eventually automate the sorting.Images were captured of ferrous and non-ferrous materials. The images areprocessed and segmented to be used as annotation data for a deep convolutionalneural segmentation network. Network models have been trained on different kinds and amounts of data. The resulting models are evaluated and tested in ac-cordance with different evaluation metrics. Methods of creating advanced train-ing data by merging imaging information were tested. Experiments with using classifier prediction confidence to identify objects of unknown classes were per-formed. This thesis shows that it is possible to discern ferrous from non-ferrous mate-rial with a purely vision based system. The thesis also shows that it is possible to automatically create annotated training data. It becomes evident that it is possi-ble to create better training data, tailored for the task at hand, by merging image data. A segmentation network trained on more than two classes yields lowerprediction confidence for objects unknown to the classifier.Substituting manual sorting with a purely vision based system seems like aviable approach. Before a substitution is considered, the automatic system needsto be evaluated in comparison to the manual sorting.
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