A Modular Approach to Detection and Identification of Defects in Rough Lumber
نویسندگان
چکیده
This paper describes a prototype scanning system that can automatically identify several important defects on rough hardwood lumber. The scanning system utilizes 3 laser sources and an embedded-processor camera to capture and analyze profile and gray-scale images. The modular approach combines the detection of wane (the curved sides of a board, possibly containing residual bark) with classification of defects. For identifying clear (unblemished) wood, a multilayer perception network is used; and for other defects, statistically trained radial-basis-function networks are implemented, followed by a competitive decision scheme. The system is among the first to scan and evaluate lumber in its rough (unplaned) state.
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