Building a Computer Vision System for Autonomous Tree Planting: Using YOLO and U-Net to Find Planting Spots in Clear-Felled Areas

Abstract

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.