External Hardwood Log Scanning and Internal Defect Feature Prediction

Research Issue

Automatically locating and classifying log defects helps to improve lumber yield, in terms of both volume and quality.  Traditional defect inspection is done by the sawyer’s naked eye within a matter of seconds.  Such visual inspection has a high error rate, and is easily influenced by the operator’s physical and mental condition.  Thus, this has become an active research area where a variety of computerized defect detection and classification systems are being examined by various research groups to assist the sawyer’s decision-making abilities.

Our Research

A test-bed laser log scanning system has been developed and is installed at the Princeton WV Laboratory.  The system generates high-resolution surface scans of logs using off-the-shelf industrial components.  Ed Thomas has developed a series of statistical models that accurately predict internal defect features, such as depth, and internal shape, based on measurable external features.  Currently, internal prediction models exist for red oak (Quercus rubra L.) and yellow-poplar (Liriodendron tulipifera L.).  

Dr. Thomas at Concord University is working with the defect detection methods using the high-resolution laser scan data.  The detection methods currently work by detecting defects with a significant height rise when compared to the surrounding log area. These methods may be improved by examining bark texture to determine less severe defects.  

 Dr. Wang at West Virginia University is developing grade sawing heuristic algorithms. These algorithms process the laser scanned log shape and internal defect feature data to determine the sawing strategy that will yield the highest valued board combination.

Expected Outcomes

The main goal of this research effort is to provide an economical method of 

1. Scanning hardwood logs
2. Locating external defects and determining internal defect features.
3. Determining the best sawing strategy for each log given its shape and defect features.

In addition, this research seeks to provide information about the nature and predictability of hardwood log defects to foresters, loggers, and other researchers.

Research Results

Thomas, R. Edward. 2009. Modeling the relationships among internal defect features and external Appalachian hardwood log defect indicators. Silva Fennica. Vol. (3). http://www.metla.fi/silvafennica/

Thomas, R. Edward. 2009. Hardwood log defect photographic database, software and user's guide. Gen. Tech. Rep. NRS-40. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 21 p.

Thomas, Edward; Thomas, Liya; Shaffer, Clifford A. 2008. Defect detection in hardwood logs using high resolution laser scan data. In: Proceedings of the 15th international symposium on nondestructive testing of wood; 2007 September 10-12; Duluth, MN. Duluth, MN: Natural Resources Research Institute, University of Minnesota Duluth: 163-167.

Thomas, R. Edward. 2008. Predicting internal yellow-poplar log defect features using surface indicators. Wood and Fiber Science. 40(1): 14-22. 

Thomas, R. Edward; Liya Thomas, Clifford A. Shaffer, Lamine Mili. 2007. Using external high-resolution log scanning to determine internal defect characteristics. In Proc, 15th Central Hardwood Forest Conference. Buckley, David S.; Clatterbuck, Wayne K.; [Editors]. e-Gen. Tech. Rep. SRS–101. U.S. Department of Agriculture, Forest Service, Southern Research Station. 770 p. [CD-ROM]. 

Thomas, L., Shaffer, Mili, E. Thomas. 2007. Algorithm detection of severe surface defects on barked hardwood logs and stems. Forest Prod. J. 57(4):50-56. • Thomas, L.; L. Mili, E. Thomas, C.A. Shaffer. 2006. Defect detection on hardwood logs using laser scanning. Wood and Fiber Sci. 38(4):682-695. 

Liya Thomas, Lamine Mili, Clifford Shaffer, Ed Thomas. 2004. Defect detection on hardwood logs using high resolution three-dimensional laser scan data. IEEE Int. Conference on Image Processing, ICIP, Singapore, October 24-27, 2004, 243-246. 

Research Participants

Principal Investigator

• Ed Thomas, USDA Forest Service - Northern Research Station, Research Computer Scientist

Research Partner

• Dr. Liya Thomas, Concord University, Athens, WV 
• Dr. Jingxin Wang, West Virginia University, Morgantown, WV  
• Dr. Clifford Shaffer, Virginia Tech. Blacksburg, VA

For further details log on website :
http://www.nrs.fs.fed.us/sustaining_forests/conserve_enhance/timber/thinning_burning_grid/