Show simple item record

dc.contributor.authorMatovic, Mark Phillip
dc.date.accessioned2021-05-07T07:41:05Z
dc.date.available2021-05-07T07:41:05Z
dc.date.issued2020-12-22
dc.identifier.citationMatovic, M.P. (2020). Uganda Traffic Sign Recognition System Using Deep Learning. (Unpublished undergraduate dissertation) Makerere University. Kampala, Ugandaen_US
dc.identifier.urihttp://hdl.handle.net/20.500.12281/10594
dc.descriptionA Report submitted in partial fulfillment of the requirements for the Degree of Bachelor of Science in Electrical Engineering of Makerere University.en_US
dc.description.abstractIncrease in the number of vehicles on road necessitates the use of automated systems for driver assistance. These systems form important components of self-driving vehicles also. Traffic Sign Recognition system is such an automated system which provides contextual awareness for the self-driving vehicle. CNN based methods like Faster R-CNN for object detection provide human level accuracy and real time performance and are proven successful in Traffic Sign Recognition systems. Single stage detection systems such as YOLO and SSD offer state-ofthe-art realtime detection speed. In this report we design a traffic sign recognition system by applying deep learning techniques in particular using the Sinlge-Shot Detector algorithm. The network training and evaluation are done using a Ugandan traffic sign detection dataset that we collected ourselves as part of the project. We detect only a subset of the Ugandan traffic signs considering the shortage of data for some traffic signs.en_US
dc.language.isoenen_US
dc.publisherMakerere Universityen_US
dc.subjectTraffic Signen_US
dc.subjectDeep Learning.en_US
dc.titleUganda Traffic Sign Recognition System Using Deep Learning.en_US
dc.typeThesisen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record