EVALUATION OF TEST SPECIMEN SURFACE PREPARATION ON COMPUTER VISION WOOD IDENTIFICATION
Abstract
Previous studies on computer vision wood identification (CVWID) have assumed or implied that the quality of sanding or knifing preparation of the transverse surface of wood specimens could influence model performance, but its impact is unknown and largely unexplored. As a preliminary step in investigating the possible effects of surface preparation quality, this study evaluates the predictive accuracy of a previously published 24-class model, trained on images from Peruvian wood specimens prepared at 1500 sanding grit, with testing images from specimens (not used for training) prepared across a series of progressively coarser sanding grits (1500, 800, 600, 400, 240, 180 and 80) and high-quality knife cuts. The results show that while there was a drop in performance at the lowest sanding grit of 80, most of the higher grits and knife cuts did not exhibit statistically significant differences in predictive accuracy. These results lay the groundwork for a future larger-scale investigation into how the quality of surface preparation in both training and testing data will impact CVWID model accuracy.
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