Conveyor belts are essential components used in the mining and mineral processing industry to transport granular material and objects. However, foreign objects/anomalies transported along the conveyor belts can result in catastrophic and costly consequences. A solution to the problem is to use machine vision systems based on AI algorithms to detect anomalies before any incidents occur. However, the challenge is to obtain sufficient training data when images containing anomalous objects are, by definition, scarce. This thesis investigates how synthetic data generated by a granular simulator can be used to train a YOLOv8-based model to detect foreign objects in a real world setting. Furthermore, the domain gap between the synthetic data domain and real-world data domain is bridged by utilizing style transfer through CycleGAN. Results show that using YOLOv8s-seg for instance segmentation of conveyors is possible even when trained on synthetic data. It is also shown that using domain adaptation by style transfer using CycleGAN can improve the performance of the synthetic model, even when the real-world data lacks anomalies.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-227520 |
| Date | January 2024 |
| Creators | Fridesjö, Jakob |
| Publisher | Umeå universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UMNAD ; 1503 |
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