Obstacle Detection and Avoidance System for Unmanned Aerial Vehicles Based on Monocular Camera

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dc.contributor.advisorLeung, Henry
dc.contributor.authorYu, Mingrui
dc.contributor.committeememberBisheban, Mahdis
dc.contributor.committeememberCarriere, Jay
dc.date2024-11
dc.date.accessioned2024-09-27T21:57:10Z
dc.date.available2024-09-27T21:57:10Z
dc.date.issued2024-09-27
dc.description.abstractUnmanned Aerial Systems (UAS), commonly known as drones, are aircraft systems without a human pilot onboard, controlled remotely or autonomously. Algorithms like YOLO (You Only Look Once) for object detection and pathfinding algorithms like A* (A-Star) can quickly navigate around large, static objects like buildings or trees. However, detecting small objects and handling dynamic aerial environments remain challenging. To address this, we introduce an innovative system for small object detection and real-time path planning using a monocular camera. Our dual-stage system combines traditional detection methods like background subtraction with advanced deep-learning techniques for improved reliability to create initial detection zones, further refined by target tracking methods for increased accuracy and depth predictor for getting estimated distance. Additionally, we have developed a new path planning algorithm, Circle Rapidly-exploring Random Trees-star (Circle RRT*), for effective obstacle avoidance. Our Obstacle Detection and Avoidance architecture navigates dynamic conditions with greater precision and speed in identifying small targets.
dc.identifier.citationYu, M. (2024). Obstacle detection and avoidance system for unmanned aerial vehicles based on monocular camera (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/119927
dc.language.isoen
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectmachine learning
dc.subjectuav
dc.subject.classificationEducation--Industrial
dc.subject.classificationEducation--Technology
dc.titleObstacle Detection and Avoidance System for Unmanned Aerial Vehicles Based on Monocular Camera
dc.typemaster thesis
thesis.degree.disciplineEngineering – Electrical & Computer
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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