journal article·1998-11

NONDESTRUCTIVE EVALUATION OF HARDWOOD LOGS: CT SCANNING, MACHINE VISION AND DATA UTILIZATION

Daniel L. Schmoldt, Luis G. Occeña, A. Lynn abbott, Nand K. Gupta

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Abstract

Abstract Sawing of hardwood logs still relies on relatively simple technologies that, in spite of their lack of sophistication, have been successful for many years due to wood's traditional low cost and ready availability. These characteristics of the hardwood resource have changed dramatically over the past 20 years, however, forcing wood processors to become more efficient in their operations. In spite of some recent advances, the breakdown of hardwood logs into lumber continues to be hampered by the inability of sawyers to "see" inside of the log prior to making irreversible cutting decisions. The need for noninvasive assessment of hardwood logs prior to breakdown is well accepted, but is difficult to realize because industrial scanning, in this context, is unique in several respects. For example, large volumes of material must be inspected quickly over an extended duty cycle, the wood material still possesses relatively low value compared to other industrial materials that require internal scanning, and many wood processors are small operations located in rural areas. Successful implementation of new scanning technology, however, will have tremendous payback for wood processors, and for timber resource conservation efforts. The research program reviewed here applies a three-pronged approach to address this situation.First, a relatively new and innovativeCTscanning technology is being developed that can sean hardwood logs at industrial speeds.Second, machine vision software has beeen created that can interpret scanned images rapidly and with high accuracy.Third, we have developed 3-D rendering and analysis techniques that will enable sawyers to apply image assessment to actual log
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Research context

OpenAlex topics: Remote Sensing and LiDAR Applications · Industrial Vision Systems and Defect Detection · Image and Object Detection Techniques

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