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Object Detection in Object Tracking System for Mobile Robot Application
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Objektdetektering för ett objektspårningssystem applicerat på en servicerobot (Swedish)
Abstract [en]

This thesis work takes place at the Emerging Technologies department of Volvo Construction Equipment(CE), in the context of a larger project which involves several students. The focus is a mobile robot built by Volvo for testing some AI features such as Decision Making, Natural Language Processing, Speech Recognition, Object Detection. This thesis will focus on the latter. During last 5 years researchers have built very powerful deep learning object detectors in terms of accuracy and speed. This has been possible thanks to the remarkable development of Convolutional Neural Networks as feature extractors for Image Classification. The purpose of the report is to give a broad view over the state-of-the-art literature of Object Detection, in order to choose the best detector for the robot application Volvo CE is working with, considering that the robot's real-time performance is a priority goal of the project. After comparing the different methods, YOLOv3 seems to be the best choice. Such framework will be implemented in Python and integrated with an object tracking system which returns the 3D position of the objects of interest. The result of the whole system will be evaluated in terms of speed and precision of the resulting detection of the objects.

Abstract [sv]

Detta arbete utförs hos Emerging Technologies på Volvo Construction Equipment(CE) i ett stort projekt som involverar flera studenter. Arbetes fokus är att använda en robot skapad av Volvo för att testa olika AI tekniker såsom beslutsfattandeg, naturlig språkbehandling, taligenkänning, objektdetektering. Denna uppsats kommer att behandla den sistnämnda tekniken.

Under de 5 senaste åren har forskning visat att det är möjligt att bygga kraftfulla deep learning object detectors vad gäller att korrekt identifera samt snabbt detektera objekt. Allt detta är möjligt tack vare ramverket Convolutional Neural Networks som agerar som feature extractors för Image Classification. Målet med denna rapport är att ge en generell överblick över det senaste inom objektdetektering för att på så sätt välja den mest lämpliga metoden att implementera på en robot hos Volvo CE. Att ta hänsyn till realtidspresetanda är ett av målen med projeketet. Efter att ha utvärderat olika metoder valdes YOLOv3. Detta ramverk implmenterades med Python och integrerades med ett objektidentiferingssystem vilket retunerar en position i tre dimentioner. Hela systemet kommer att utvärderas med hänsyn till hastighet och presition.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:090
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-252561OAI: oai:DiVA.org:kth-252561DiVA, id: diva2:1320122
External cooperation
Volvo Construction Equipment
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2019-06-04 Created: 2019-06-04 Last updated: 2019-06-04Bibliographically approved

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CiteExportLink to record
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