Introduction ation, type, and severity of external defects on Before a hardwood log is processed, it is visually aslogs and stems are the primary indicators of sessed and the number and type of external defects noted. quality and value. External defects provide The difference between high and low &ty logs is deterinternal log characteristics. Anticipating the mined by defect type, frequency, size, and location. It is on and characteristics of internal log defects prior difficult to accurately and rapidly detect and measure debreakdown promises to dramatically improve the fects, either mechanically or manually (Tian and Murphy on of lumber in terms of both quality and quan1997). But, optimizing the breakdown of the log into ing industrial scanning components, a high-resoproducts is a crucial step. The value of the lumber that laser scanning system has been constructed in can be recovered depends on the presence and location of on, West Virginia, to examine the potential of ladefects. This is especially true for hardwood logs. In the scanning for defect detection. To date, 162 red oak production of hardwood lumber, boards are sawn to .rmd yellow-poplar logs have been scanned and processed. fixed thicknesses and random widths. Since the presUsing robust circle or ellipse fitting methods, a residual ence, size, and placement of defects on the boards affect image is extracted from the laser scan data. The log board quality and value, much attention is focused on log in the residual images show most bark texture surface defects during processing. h tu res and surface characteristics of the original log or For every surface indicator there is almost always an stem. Defects with height differentiation from the backassociated internal defect. ExternaI defect indicators (on epund log surface can be distinguished. Using simple the surface of the log, as opposed to the log ends) are shape definition rules ~ornbined with the height map albumps, splits, holes, and circular distartions in the bark lows most severe defects to be detected. Defects can be pattern. Bumps usually indicate overgrown knots, detected by determining the contour levels of a residual branches, or wounds. Some bumps have a cavity or hole Smage. Pattern recognition methods, such as cluster in the middle, indicating &at the overgrown m a t e d has analysis, are used to classify different defect types. With decay or is rotten. Circular bark distortions, or rings the exceptional resolution of the current scanner, we plan around a central flattened area, indicate a branch that to examine bark texture changes such that adventitious was overgrown many years earlier. Surface defects progbuds, minor distortions, and other defects not associated ress from a p-ed or broken branch to an overgrown with a height change might be detected. knot characterized by a significant bump and then to a rotten knot or a distartion defect. For some classes of defects, it is possible to accurately predict internal features based on extmal characteristics. Studies have demonstrated that the use of external or internal defect data improves cutting strategies that optiThomas: mize lumber recovery from logs, i.e., preserving the largUSDA Forest Service, Princeton, West V i . USA Thomas and Shaffe~ est possible area of clear wood on a board face (Steele et W-a Tech, Computer Science Department, BlaCksbUTg, al. 1994). In Steele et d.'s study, 12 red oaklogs were colWrginia, USA lected and divided into two groups that were as closely Thomas, Thomas, and Shaffer I matched as possible with respect to log size and q d t y . Logs from one of the groups were sliced into 0.25-in.thick discs and the location and size of all defects recorded. The data from the slices were assembled to create virtual logs showing all exterior and interior defects. The logs from the unsliced group were sawn to the best of a sawyer's ability to produce the highest valued lumber possible. The logs in the virtual group were sawn by computer using the available defect information. The logs sawn using the defect information averaged 1 1 -2 1 percent higher value than those sawn manualIy without defect information. Several scanning and optimization systems are availa&~t&dhbS&VdP&~f~~;U?L!&3 ~ ~ k ~ . TQZQQJ~W of defect detection are used on hardwood logs: internal and external. Various internal defect inspection methods have been proposed in the literature based on X-raylCT (computed tomography), X-ray tomosynthesis, MRI (magnetic resonance imaging), microwave scanning, dtrasound, and enhanced pattern recognition of regular X-ray images (Guddanti and Chang 1998, Schmoldt 1996, Wagner et al. 1989, f i u et al. 1991). CT and MRI systems provide excellent internal images of logs, but image acquisition is slow and expensive. In addition, variable moisture content and lag size can present problems to CT scanning (Bhandakar et al. 1999). Laser-line scanners are commonly used in sawmills to gather informat i ~ n on external log characteristics, e.g., diameter, taper, curvature, and length (Samson 1993). Optimization systems use the lasw-profile information to better position the log on the carriage and improve the sawyer's decision-making ability. These systems typically were developed for softwood (e.g., pine, spruce, fir) log processing. But, they are becoming increasingly commonplace in hardwood mills as well. Qur research takes the three-dimensional log surface image and processes it to determine the location of the most severe external defects: overgrown knots, rotten knots, holes/gouges, and removed branches. These types of defects are usually associated with a significant surface rise or depression depending on the defect type. The image is processed using a robust statistical approach to generate a height map of the log. Defects are characterFigure 1.4ample scanned log in dot cloud format fiom scanner used in 2001. 15th International Syl ized and located by a height change from the surrounding log area. Many internal aspects of the defect can be predicted. This system is currently under development and is expected to permit an automated approach to determining interior defect information that is faster and less expensive than other internal detection methods.
[1]
S. Guddanti,et al.
Replicating sawmill sawing with TOPSAW using CT images of a full-length hardwood log
,
1998
.
[2]
D. Ruppert.
Robust Statistics: The Approach Based on Influence Functions
,
1987
.
[3]
Suchendra M. Bhandarkar,et al.
Machine Vision and Applications c ○ Springer-Verlag 1999 CATALOG: a system for detection and rendering of internal log defects using computer tomography
,
1997
.
[4]
L. Mili,et al.
Automated detection of severe surface defects on barked hardwood logs
,
2007
.
[5]
Clifford A. Shaffer,et al.
DEFECT DETECTION ON HARDWOOD LOGS USING LASER SCANNING
,
2007
.
[6]
W. Gander,et al.
Fitting of circles and ellipses: Least squares solution
,
1994
.
[7]
Philip H. Steele,et al.
Increased lumber value from optimum orientation of internal defects with respect to sawing pattern in hardwood sawlogs
,
1994
.
[8]
Lamine Mili,et al.
Robust state estimation based on projection statistics
,
1996
.