  Wood logs picking is a challenging task to automate. Indeed, logs usually
come in cluttered configurations, randomly orientated and overlapping. Recent
work on log picking automation usually assume that the logs' pose is known,
with little consideration given to the actual perception problem. In this
paper, we squarely address the latter, using a data-driven approach. First, we
introduce a novel dataset, named TimberSeg 1.0, that is densely annotated,
i.e., that includes both bounding boxes and pixel-level mask annotations for
logs. This dataset comprises 220 images with 2500 individually segmented logs.
Using our dataset, we then compare three neural network architectures on the
task of individual logs detection and segmentation; two region-based methods
and one attention-based method. Unsurprisingly, our results show that
axis-aligned proposals, failing to take into account the directional nature of
logs, underperform with 19.03 mAP. A rotation-aware proposal method
significantly improve results to 31.83 mAP. More interestingly, a
Transformer-based approach, without any inductive bias on rotations,
outperformed the two others, achieving a mAP of 57.53 on our dataset. Our use
case demonstrates the limitations of region-based approaches for cluttered,
elongated objects. It also highlights the potential of attention-based methods
on this specific task, as they work directly at the pixel-level. These
encouraging results indicate that such a perception system could be used to
assist the operators on the short-term, or to fully automate log picking
operations in the future.
