Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/75496
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dc.contributor.authorTan, Zhi Wei-
dc.date.accessioned2018-05-31T09:07:27Z-
dc.date.available2018-05-31T09:07:27Z-
dc.date.issued2018-
dc.identifier.urihttp://hdl.handle.net/10356/75496-
dc.description.abstractSelf-driving cars developments are becoming the norm in both automotive and IT industry. Recent researches were focusing on ways these autonomous vehicles interact with its environment. Object detection using cameras is one of the important vision aids and they are used for applications such as path planning, object avoidance and localizations. In this study, the particular case of You Only Look Once v2 on the Udacity crowdAI data set was analyzed for its performance on different sizes of object classes. This work improves on the object performance of the original detection model for Car by an average of 12% on the VOC2007 Average Precision for all sizes. Specifically, the Small-Car and Small-Truck improved by 2.8% and 4% on the same metric.en_US
dc.format.extent61 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University-
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleSmall object recognition using machine learning techniquesen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorHuang Guangbinen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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