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Evaluation of a multi-sensor machine vision system for automated hardwood lumber gradingAuthor(s): D. Earl Kline; Chris Surak; Philip A. Araman
Source: Proceedings, 4th International Conference on Image Processing and Scanning of Wood. 75-87.
Publication Series: Miscellaneous Publication
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DescriptionOver the last 10 years, scientists at the Thomas M. Brooks Forest Products Center, the Bradley Department of Electrical Engineering, and the USDA Forest Service have been working on lumber scanning systems that can accurately locate and identify defects in hardwood lumber. Current R&D efforts are targeted toward developing automated lumber grading technologies. The objective of this work is to evaluate hardwood lumber grading accuracy based on current state-of-the-art multiple sensor scanning technology which uses laser profile detectors, color cameras, and an x-ray scanner. 89 red oak boards were scanned and graded using Virginia Tech's multiple sensor scanning system. A certified National Hardwood Lumber Association (NHLA) employed lumber inspector then graded the lumber and the boards were manually digitized and mapped for defects. The lumber grading system was found to be 63 percent accurate in classifying board grade on a board-by-board basis. While this accuracy may seem low, the automated lumber grading system was found to be 31 percent more accurate than the line graders. Further, the automated lumber grading system estimated the lumber value to be within 6 percent of the NHLA certified value whereas the line grader overestimated the lumber value by close to 20 percent. Most automated lumber grading discrepancies resulted from board geometry related issues (e.g. board crook, surface measure rounding, calculation of cutting units, etc.). Concerning the multiple sensor scanning system, defect recognition improvements should focus on better methods to differentiate surface discoloration from critical grading defects. These results will help guide the development of future scanning hardware and image processing software to more accurately identify lumber grading features.
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CitationKline, D. Earl; Surak, Chris; Araman, Philip A. 2000. Evaluation of a multi-sensor machine vision system for automated hardwood lumber grading. Proceedings, 4th International Conference on Image Processing and Scanning of Wood. 75-87.
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