Machine Vision for Knot Detection and Location in Chinese Fir Lumber

Abstract

Abstract In order to be utilized in the design of a wood building, the lumber must pass grade. Machine-vision inspection grading offers higher efficiency and accuracy than traditional manual visual grading. In this paper, a fast and accurate method for identifying defects in large-size structural lumber based on machine vision of Fujian Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) constructional lumber (FCF CL) is proposed. Specifically, the grey matrix of the captured images on the surface of the sawn timber is initially scanned and the pixel weights on the edges of the image greyness variables are calculated. A matrix-valued torus was formed by fitting the knot edge profile and analyzing changes in the gradient values at the knot's edge, as well as calculating the directional derivative's rate of change. The knot three-dimensional mapping curves were projected onto the plane to form horizontal rise contours. Observe from the contour map of the whole large-size sawn timber, and extract the positional information of the knot where there is a trough (groove). The test results show that the rRMSE (Relative Root Mean Square Error) measured at the x axis position of knots is within 0.49 percent; the rRMSE measured at the y axis is 0.35 percent, which has high detection accuracy and meets the production requirements. We also investigated the effect of knots in different positions on the modulus of elasticity and the bending strength of FCF CL, with a view to establishing a link between machine-vision knot detection and mechanical properties of sawn timber in our next work.