Automatic Classification of Compression Wood in Green Southern Yellow Pine
Keywords:Compression wood, color scanning, X-ray scanning, nondestructive evaluation, machine vision, image processing
AbstractCompression wood is a feature in softwoods that is undesired in sawn wood products due to its tendency to bend and crook as the moisture content changes. An automatic compression-wood detection method was developed and tested on southern yellow pine lumber in the green condition. Sixteen lumber specimens were scanned using both a color camera and an X-ray scanner. Color information was shown to have significant and consistent differences between compression wood and clear wood. However, X-ray information was found to contain large density variations in green lumber due to inconsistent moisture content that would mask density variations arising from compression wood. Therefore, it was concluded that X-ray information would not be useful in detecting compression wood in green southern yellow pine lumber. A multivariate regression model was developed based only on color information from one of the board samples. A nonlinear prediction model was produced by using the original color image data and expanded variables derived from the color images. The model based on one board sample was then applied on all boards. Classified images of the board surfaces were produced and compared to manually detected compression wood. An overall accuracy of 87% was observed in the classification of compression wood.
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