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Tree species classification with YOLOv3: Classification of Silver Birch (Betula pendula) and Scots Pine (Pinus sylvestris)
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Trädartsklassificering med YOLOv3 (Swedish)
Abstract [en]

Automation of tree species classification during a forest inventory could potentially provide more efficiency and better results for forest companies and stakeholding agencies. This thesis investigates how well a state of the art object detection system, YOLOv3, performs this classification task. A new image dataset with pictures of Silver Birches and Scots Pines, called LilljanNet, was created to train YOLOv3. After training YOLOv3 on half the dataset we performed validation by testing it against the other half. The trained model scored a mean average precision above 0.99. Training was also done with smaller sets of training data and the mean average precision score for these models all achieved mean average precision above 0.95. The results are promising and further research should be done testing this on smartphones and drones.

Abstract [sv]

Automatisering av trädslagsklassifiering vid en skogstaxering skulle potentiellt sätt kunna ge mer effektivitet och bättre resultat för skogsbolag och myndigheter som ansvarar för skogen. Denna uppsats undersöker hur väl ett toppmodernt datorseendesystem, YOLOv3, utför denna klassifieringsuppgift. Ett nytt bildbibliotek med bilder av björkar och tallar, som kallas LilljanNet, skapades för att träna YOLOv3. Efter vi tränat YOLOv3 på halva datamängden utförde vi validering mot den andra halvan. Den upptränade modellen uppnådde ett mean average precision över 0.99. Träning gjordes också med mindre mängder träningsdata och mean average precision-resultaten för dessa modeller var alltid över 0.95. Resultaten är lovande och mer forskning bör göras där man testar att implementera detta på smartphones och drönare.

Place, publisher, year, edition, pages
2019. , p. 25
Series
TRITA-EECS-EX ; 2019:378
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-260244OAI: oai:DiVA.org:kth-260244DiVA, id: diva2:1354883
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Examiners
Available from: 2019-10-17 Created: 2019-09-26 Last updated: 2019-10-17Bibliographically approved

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