Defect Detection on Hardwood Logs Using Laser Scanning


  • Liya Thomas
  • Lamine Mili
  • Edward Thomas
  • Clifford A. Shaffer


Computer vision, defect detection, robust estimation method, scanning log laser


To improve the sawyer's ability to process hardwood logs and stems, and thereby generate a higher valued product, automated detection methods of external defects have been developed and successfully tested on a large collection of real log samples. Since external defects provide hints about internal log characteristics, the location, type, and severity of external defects are the primary indicators of overall hardwood log quality and value. Using a high-resolution laser log scanner supplied by Perceptron, 162 red oak and yellow-poplar logs were scanned and digitally photographed. By means of 2-D circles fitted using a robust estimation method, a residual image is extracted from the laser scan data. Other robust fitting methods, such as ellipse and cylinder fitting, also are examined and their performance is evaluated. Our investigation reveals that the residuals, which are defined as the radial distances between the data points and the fitted curves or surfaces, provide valuable information about defects exhibiting height differentiation from the log surface. In other words, the log "skins" in the residual images show most bark texture features and surface characteristics of the original log or stem. Based on the contour levels estimated from a residual image, the developed methods allow us to detect most severe defects using a combination of simple shape definition rules with the height map. Less significant, yet severe defects are pinpointed using a shape profile.


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