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1	Teaching an AI to recycle by looking at scrap metal : Semantic segmentation through self-supervised learning with transformers / Lär en AI att källsortera genom att kolla på metallskrot Forsberg, Edwin, Harris, Carl January 2022 (has links) Stena Recycling is one of the leading recycling companies in Sweden and at their facility in Halmstad, 300 tonnes of refuse are handled every day where aluminium is one of the most valuable materials they sort. Today, most of the sorting process is done automatically, but there are still parts of the refuse that are not correctly sorted. Approximately 4\% of the aluminium is currently not properly sorted and goes to waste. Earlier works have investigated using machine vision to help in the sorting process at Stena Recycling. However, consistently through all these previous works, there is a problem in gathering enough annotated data to train the machine learning models. This thesis aims to investigate how machine vision could be used in the recycling process and if pre-training models using self-supervised learning can alleviate the problem of gathering annotated data and yield an improvement. The results show that machine vision models could viably be used in an information system to assist operators. This thesis also shows that pre-training models with self-supervised learning may yield a small increase in performance. Furthermore, we show that models pre-trained using self-supervised learning also appear to transfer the knowledge learned from images created in a lab environment to images taken at the recycling plant. AI self-supervised learning SSL Machine vision ML Semantic segmentation Transformer Swin-transformer Barlow twins DINO SwAV Recycling AI SSL Datoreseende maskininlärning semantisk segmentering Transformer Swin-transformer Barlow twins DINO SwAV Återvinning

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Teaching an AI to recycle by looking at scrap metal : Semantic segmentation through self-supervised learning with transformers / Lär en AI att källsortera genom att kolla på metallskrot