Building a Computer Vision System for Autonomous Tree Planting: Using YOLO and U-Net to Find Planting Spots in Clear-Felled Areas
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2022
Författare
Christenson, Olle
Lundgren, Jens
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Planting new saplings at clear-felled areas is an expensive and demanding task for forest owners. To improve
efficiency and consistency, Södra Skogsägarna has initiated a project to develop an autonomous vehicle to plant
new saplings in clear-felled areas. A crucial function of the system is how to select the planting spots.
This thesis aims to create a deep learning-based computer vision model to locate favorable planting spots.
A stereo camera that provides RGB-D data from different scenes, where a sapling should be planted, will
be used. The created model takes this data as input and returns the coordinates of two proposed planting spots. The model is based on a YOLO network for object detection and two different implementations
of U-Net networks for segmentation. The algorithm was able to find good planting spots in 81% of the test cases.
A discussion of the most common reasons why the model occasionally proposes invalid planting spots and
suggestions on how to solve these problems are given. Suggestions are also given on how the project group could
proceed with the project and improve the system. The main conclusions are that a better suited camera than the
one used in this thesis should be used and that more data should be collected to increase the models robustness.
The code for the final system can be found in the repository https://github.com/Birken666/Master-Thesis.
Beskrivning
Ämne/nyckelord
Computer Vision , Stereo Vision , Machine Learning , Object Detection , Semantic Segmentation , Forestry , Tree Planting