Object detection and machine learning are important fields in Computer science. This report presents two methods to find the bounding box of a license plate and tries to evaluate the best approach to deal with various lighting conditions. The first method uses edge detection to find a number of potential candidates, where each candidate is fed to a machine learning model who decides if the candidate is a license plate or not. This had an accuracy of 39%. This method is pointing towards struggling with varying light levels and the lowest accuracy was measured at the highest and lowest mean brightness values. The second method uses mostly machine learning to find the bounding box of a license plate which achieved a higher accuracy with 68%. This method seems to be better in low-light conditions and is more uniform in accuracy across different lighting conditions.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-125776 |
| Date | January 2023 |
| Creators | Sturesson, André, Böök, Johannes |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| 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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