Multimodal surface defect detection from wooden logs for sawing optimization

Abstract

We propose a novel, cost-effective method for knot detection on the surface of wooden logs using multimodal data fusion. Knots are a primary factor affecting the quality of sawn timber, making their detection fundamental to any timber grading or cutting optimization system. However, due to the small size of knots and distortions caused by factors such as bark and other natural variations, detection accuracy often remains low when using only one measurement modality. Previous work on the sawing optimization use case primarily focused on X-ray computed tomography data, which provides accurate knot locations and internal structures, it is often too slow or expensive for practical use. An attractive alternative is to use fast and cost-effective log surface measurements, such as laser scanners or cameras, to detect surface knots and estimate the internal structure of wood. In this paper, we demonstrate that by using a data fusion pipeline consisting of separate streams for image and point cloud data, combined by a late fusion module, higher knot detection accuracy can be achieved compared to using either modality alone. We further propose a simple yet efficient sawing angle optimization method that utilizes surface knot detections and cross-correlation to minimize the amount of unwanted arris knots, demonstrating its benefits over randomized sawing angles.