This thesis evaluates the AMLAS methodology. To support the evaluation, literature studies are conducted and a machine learning dependent system that detects people and helmets is implemented. The practical work is performed according to the documentation of AMLAS. Alongside this work, a user interface is developed. The user interface and the machine learning component is merged to create the complete system. The results show that AMLAS contributes with safety, structure and reliability to the system. However, the findings show that AMLAS is missing some aspects. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-186649 |
| Date | January 2022 |
| Creators | Hamnert, Josef, Hägglund, Daniel |
| Publisher | Linköpings universitet, Medie- och Informationsteknik, Linköpings universitet, Tekniska fakulteten |
| 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 |
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