CNN-based Method for Segmenting Tree Bark Surface Singularites

The analysis of trunk shape and, in particular, the geometric structures on the bark surface are of main interest for different applications linked to the wood industry or biological studies. Bark singularities are often external records of the history of the development of internal elements. The actors of the forest sector grade the trees by considering these singularities through standards. In this paper, we propose a method using terrestrial LiDAR data to automatically segment singularities on tree surfaces. It is based on the construction of a relief map combined with a convolutional neural network. The algorithms and the source code are available with an online demonstration allowing to test the defect detection without any software installation.