Predicting Inner Wood Defects from Outer Bark Features – WoodSeer

WoodSeer is a project aiming the development of automated means for characterizing wood supply in the upstream part of the forest-wood chain in the context of inventory, log trade and first processing, by providing detailed information on the nature, location and dimensions of internal defects in an automated manner. Nowadays, for this industry, this information is only accessible through the use of X-ray CT scanners providing an internal reference structure for the rameal system, but not easily affordable for companies because of the high required investments. Nevertheless, this information is integrated through the experience and know-how of experts in the wood grading process in forests or wood-yards. Thus, the founding idea of the project is to link the signatures of defects on the bark with their internal characteristics. Without being exclusive, the targeted defects are the knots of the pruned branches included in the trunk. Indeed, the scar of a branch on the bark contains information about the height of the bud that gave birth to it, the diameter of the branch at the time of pruning, its average inclination and the radial growth of the trunk after pruning.
Thanks to previous works involving several project partners, very promising initial results were obtained on the detection of defect signatures on the surface of smooth or rough bark, using algorithms processing the 3D description of the log surface made with a terrestrial Lidar. This work also provided a classification of the defect type and the computation of relevant characteristics.
In continuity and in a first Work-Package, the WoodSeer project aims to study a diversification of the digitizing methods able to provide a three-dimensional description of the surface of a log through the use of portable devices in the forest and on industrial sites using true shape scanners, but also to assess their relevance in detecting surface defects. Some tasks are dedicated to provide data to link the external and internal description of the same defect, through the use of an X-ray scanner. Others tasks aim to complete the datasets with synthetic 3D geometric structures in order to be able to use efficient artificial intelligence methods, such as Deep Learning in the other tasks of the project.
The second Work-Package aims at improving the available methods for detecting and classifying surface defects by exploring other ways, such as combining Deep Learning and geometric constraints to facilitate the reinforcement of relevant databases in the future.
The last Work-Package is dedicated to the establishment of external-internal relationships, in order to be able to predict the internal defect from its external signature, with the ambition of relying on artificial intelligence methods such as neural networks.
To meet these objectives, the consortium gathered in this project benefits from a representativeness of the sector thanks to managers and sales representatives of the public and private forests, a sawmill, a subcontract with a company designing and assembling geometrical shape scanners and buckling optimization software. On the scientific level, WoodSeer brings together recognized expertise in 3D surface and volume digitization and their processing to extract information. The project also includes skills in tree development, its impact on wood quality, and in statistical modelling, as well as skills in discrete geometry, synthesized objects, and finally in artificial intelligence.