Oblique x-ray log scanning and knot identification

The existence and location of knots affects the material properties and commercial value of cut lumber. Specifically, knots reduce the overall strength of lumber, with knots closer to the edge having a larger negative effect. Therefore, by determining the knot locations before cutting, and tailoring the cutting patterns to place the knots optimally within the lumber, the lumber quality and value can be increased. X-rays can image the interior of a log to detect these knots; however existing methods are either too complex and costly (Computed Tomography) or lack the ability to differentiate between knots reliably (Orthogonal Radiography). This research aims to overcome these limitations by employing a novel ‘oblique’ scanning arrangement that can determine knot orientations with both reasonable accuracy and low cost. Image processing and detection algorithms were developed to locate and orientate the knots automatically within the X-ray scans, and different methods of calculating the knot’s circumferential angle compared. Detection metrics of Precision and Recall were used to analyse the performance of the detection algorithm. Finally, purpose-built hardware was designed and constructed to conduct scans of the logs. Results indicate that the oblique scanning method is a viable way to detect and orientate knots within logs with both reasonable accuracy and low cost compared to existing methods. An average circumferential angle error of 15 degrees was achieved, with the detection algorithm being able to detect between 60% to 80% of the knots present within the log for ideal tuning parameters.Applied Science, Faculty ofMechanical Engineering, Department ofGraduat