Artificial Intelligence-Driven Timber Wood Defect Characterization from Terahertz Images

Abstract

In the timber manufacturing sector, ensuring high-quality products is crucial, but conventional inspection methods often struggle to detect internal defects non-destructively. To tackle this challenge, an innovative approach has been proposed that integrates terahertz (THz) imaging with artificial intelligence (AI) algorithms. By harnessing the unique ability of THz radiation to penetrate timber wood and AI algorithms, defect classification, segmentation, and characterization can be made possible. Here, a custom-made convolutional neural network has been optimized for the classification of the defects in timber wood into four classes – no defect, knot, small knot, and decay, yielding a classification accuracy of over 96%. Further, the custom classification model has been extended for thicker wooden samples with internal hidden defects using transfer learning and has yielded a classification accuracy of over 93%. Following classification, a U-Net-based segmentation algorithm has been developed to delineate the defect boundaries in THz images accurately with a high dice coefficient of over 0.90. Further, a YOLO-based algorithm has been utilized to characterize the defects by localizing the position of the defect using bounding boxes with a high F1 score of over 0.97. An accurate prediction of the defect dimension has been demonstrated using this algorithm with a percentage error of less than 4% for all the types of defects in the timber wood. This advanced methodology, leveraging multiple AI algorithms on the THz images, significantly boosts the efficiency and accuracy of automatic defect identification and characterization, marking a transformative step forward in timber industry quality control processes.