  Vision-based segmentation in forested environments is a key functionality for
autonomous forestry operations such as tree felling and forwarding. Deep
learning algorithms demonstrate promising results to perform visual tasks such
as object detection. However, the supervised learning process of these
algorithms requires annotations from a large diversity of images. In this work,
we propose to use simulated forest environments to automatically generate 43 k
realistic synthetic images with pixel-level annotations, and use it to train
deep learning algorithms for tree detection. This allows us to address the
following questions: i) what kind of performance should we expect from deep
learning in harsh synthetic forest environments, ii) which annotations are the
most important for training, and iii) what modality should be used between RGB
and depth. We also report the promising transfer learning capability of
features learned on our synthetic dataset by directly predicting bounding box,
segmentation masks and keypoints on real images. Code available on GitHub
(https://github.com/norlab-ulaval/PercepTreeV1).
