Nondestructive rule-based defect detection and identification system in CT images of hardwood logs

Abstract

This paper is concerned with the detection of internal defects in hardwood logs. Because the commercial value of hardwood lumber is directly related to the quantity, type, and location of defects in the wood, sawing strategies are typically chosen in an attempt to minimize the defects in the resulting boards. Traditionally, the sawyer makes sawing decisions by visually examining the exterior of log and then revising the sawing strategy as more and more of the log's interior is exposed. Significantly better results would be expected if internal defects were known, so that a globally optimum solution could be selected in advance. This paper addresses this problem through the analysis of computed tomography (CT) images of logs, for the purpose of detecting important hardwood defects. In particular, the paper considers defect-dependent postprocessing methods, based on mathematical morphology, that show promising results.

Wood Veneer Defect Detection Based on Multiscale DETR with Position Encoder Net

“…With regard to traditional defect detection, Danvind used computed tomography (CT) technology to carry out nondestructive testing on logs in order to obtain the information of structural characteristics and moisture, which provides guidance for rational utilization [ 35 ]. Sarigul detected important hardwood defects through the analysis of CT images of logs, considering defect-dependent post-processing methods based on mathematical morphology [ 36 ]. Bhandarkar took advantage of computer axial tomography (CAT) images to detect internal defects [ 37 , 38 ].…”

Simulated Annealing: A Novel Application of Image Processing in the Wood Area

“…In the wood industry, some applications have been found that allow the recognition of different types of defects of the raw material [14,15]. Methods for measuring moisture [16][17][18] have also been developed.…”