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Proposal networks in object detection
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
Förslagsnätverk för objektdetektering (Swedish)
Abstract [en]

Locating and extracting useful data from images is a task that has been revolutionized in the last decade as computing power has risen to such a level to use deep neural networks with success. A type of neural network that uses the convolutional operation called convolutional neural network (CNN) is suited for image related tasks. Using the convolution operation creates opportunities for the network to learn their own filters, that previously had to be hand engineered. For locating objects in an image the state-of-the-art Faster R-CNN model predicts objects in two parts. Firstly, the region proposal network (RPN) extracts regions from the picture where it is likely to find an object. Secondly, a detector verifies the likelihood of an object being in that region.For this thesis, we review the current literature on artificial neural networks, object detection methods, proposal methods and present our new way of generating proposals. By replacing the RPN with our network, the multiscale proposal network (MPN), we increase the average precision (AP) with 12% and reduce the computation time per image by 10%.

Abstract [sv]

Lokalisering av användbar data från bilder är något som har revolutionerats under det senaste decenniet när datorkraften har ökat till en nivå då man kan använda artificiella neurala nätverk i praktiken. En typ av ett neuralt nätverk som använder faltning passar utmärkt till bilder eftersom det ger möjlighet för nätverket att skapa sina egna filter som tidigare skapades för hand. För lokalisering av objekt i bilder används huvudsakligen Faster R-CNN arkitekturen. Den fungerar i två steg, först skapar RPN boxar som innehåller regioner där nätverket tror det är störst sannolikhet att hitta ett objekt. Sedan är det en detektor som verifierar om boxen är på ett objekt .I denna uppsats går vi igenom den nuvarande litteraturen i artificiella neurala nätverk, objektdektektering, förslags metoder och presenterar ett nytt förslag att generera förslag på regioner. Vi visar att genom att byta ut RPN med vår metod (MPN) ökar vi precisionen med 12% och reducerar tiden med 10%.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:007
Keywords [en]
Deep learning, Machine learning, Computer vision, Applied mathematics, Statistics, Artificial Neural Networks, Object detection, Faster R-CNN, RPN, Proposal Network
Keywords [sv]
Maskininlärning, Neurala nätverk, Objektdetektering, Tillämpad matematik, Matematisk statistik, RPN, Förslags nätverk
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-241918OAI: oai:DiVA.org:kth-241918DiVA, id: diva2:1282823
External cooperation
Sarvai AB
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2019-01-26 Created: 2019-01-26 Last updated: 2019-01-26Bibliographically approved

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