An image is worth 10,000 points: Neural network architectures and alternative log representations for lumber production prediction

Abstract

Predicting the lumber products that can be obtained from a log allows for better allocation of resources and improves operations planning. Although sawing simulators make it possible to anticipate the production associated with a log, they do not allow processing many logs quickly. It was shown that machine learning can be used in place of a simulator. However, prediction quality is still lacking and information rich log representations are seldomly used in the literature for machine learning purposes We compare several log representations that can be used (industry know-how-based features, 2D projections, and 3D point clouds) and several neural network architectures able to process these log representations (multilayer perceptron, residual network and PointNet). We also propose a new way to implement a loss function that improves prediction of sparse object count in regression. This new approach achieves a 15% improvement of F1 score compared to previous approaches.