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Design of a Neural Network Architecture for Traffic Light Detection in Autonomous Vehicles

dc.contributor.authorAkubo, Raphael Ede
dc.description.abstractOver the ongoing years, innovatively propelled nations have kept on joining the quest for growing completely self-sufficient driven vehicles. This Autonomous vehicles intend to address issues of driver profitability and effectiveness. Dependable traffic light discovery is a vital segment for self-sufficient driving. Recognizing the traffic lights amidst everything is a standout amongst the most significant errand. The focus of this research is to develop and find the optimal parameters for an efficient Neural Network Architecture to aid a hardware engineer to implement on a hardware for the autonomous vehicle. This is done by designing an Artificial Neural Network (ANN) that would be capable of detecting and correctly classifying any traffic light within the city of Abuja, Nigeria. This study first attempts to develop a reliable traffic sign detector by constructing MLP, training using BP, and tuning various Convolutional Neural Networks (CNN). Images for training are obtained from Abuja city metropolis.en_US
dc.description.sponsorshipAUST and AfDB.en_US
dc.subject2019 Computer Science and Engineering Thesesen_US
dc.subjectAkubo Raphael Edeen_US
dc.subjectProf. Ben Abdallahen_US
dc.subjectBackpropagation (BP)en_US
dc.subjectneural networksen_US
dc.titleDesign of a Neural Network Architecture for Traffic Light Detection in Autonomous Vehiclesen_US

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  • Computer Science105

    This collection contains Computer Science Student's Theses from 2009-2022

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