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Title: Development of a machine-learning based object recognition system for quadrotors in urban environments
Authors: Lim, Brandon Yi Ming
Keywords: Engineering::Mechanical engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Project: B116
Abstract: This project presents the implementation of suitable Machine Learning (ML) architecture(s) to achieve real-time object detection and classification in a quadrotor in an urban environment, with a reasonable level of accuracy. Here, a suitable architecture refers to one that is able to achieve real-time performance, generally agreed to be 30 fps or higher among the community of ML practitioners. There is a compromise to be reached between accuracy and speed. Here, the constraint for speed is limited to the requirement of real-time performance. It is satisfactory to achieve levels of prediction accuracy comparable to current standards of reasonable accuracy, although the achievement of higher accuracy would be welcomed.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

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