Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148203
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dc.contributor.authorNgo, Jason Jun Haoen_US
dc.date.accessioned2021-04-27T08:09:34Z-
dc.date.available2021-04-27T08:09:34Z-
dc.date.issued2021-
dc.identifier.citationNgo, J. J. H. (2021). Deep learning based car license plate recognition. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148203en_US
dc.identifier.urihttps://hdl.handle.net/10356/148203-
dc.description.abstractCurrently, there is a lack of license plate recognition systems that are lightweight and fast, while still being sufficiently accurate for practical purposes. In this project, we explored various methods to adapt convolutional neural networks which fulfil the above requirements for usage on Singaporean license plates. In particular, we carried out pre-training and fine-tuning of LFFD, such that it reached an average precision of 98.99% for license plate detection. In addition, we modified the backbone architecture of LPRNet for it to handle single-row and double-row license plates, and tried out various data augmentations to improve its accuracy, such that it obtained an accuracy of 93.79% for license plate recognition. We then combined the two models to create a system that is able recognised the license plate number given an image of a Singaporean vehicle. This system is lightweight, having only a total size of 7.6 MB, and fast, taking 82 ms to process an image on average. It also has a decent recognition accuracy of 86.04%.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Image processing and computer visionen_US
dc.titleDeep learning based car license plate recognitionen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLoke Yuan Renen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.organizationOmniVision Technologies Singaporeen_US
dc.contributor.supervisoremailyrloke@ntu.edu.sgen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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