A Simple Machine Vision System for Improving the Edging and Trimming Operations Performed in Hardwood Sawmills
Hardwood timber is a substantial economic staple in the eastern U.S., where primary hardwood processors produce more than 10 billion board feet of sawn hardwoods annually. There are over 3,500 sawmills producing hardwood lumber in the Southeastern portion of the United States. Present trends such as increasing labor costs and limited supplies of high quality logs have forced hardwood lumber manufacturers to increase their efforts to maximize the utilization of this raw material. In order to make money in such a competitive business, these sawmills must produce the highest possible grade of lumber from each saw log they process.
Of all the primary and secondary processing procedures that are used to transform round wood into a final product, the sawmill edging and trimming operations have the most substantial effect on the grade and, hence, the value of the material produced. Currently, the grading of rough hardwood lumber is done manually by human inspectors according to standardized grading rules developed by the National Hardwood Lumber Association (NHLA, 1994). Standard hardwood edging and trimming operations are less than optimum because of the complexity of the grading rules, the complexity of the decision making processes involved, possible operator fatigue, and the imprecision with which lumber can be sawn by the available equipment.
Studies have shown that there is a potential to increase hardwood lumber value by over 20 percent if optimum edging and trimming could be performed in hardwood sawmills. Even a small portion of this percentage would substantially increase the profit of hardwood lumber manufacturers. And this can be achieved just by utilizing some degree of automation. That is, some type of system must be designed that can scan a board to sense important hardwood features, make correct edging and trimming decisions, and then control down stream edgers and trimmers with minimal operator intervention. The most difficult part in the development of this automatic edging and trimming system is to get enough major defect information to make very good edging and trimming decisions.
This thesis describes the research that was performed to build a prototype system that can collect images of boards and extract major defect information for making good edging and trimming decisions. The images that are collected include Black/White and laser profile images. Necessary defect information to be extracted for making edging and trimming decisions includes the location and size of large grading defects and areas of the board that are too thin. This thesis talks about the hardware that was used for collecting the needed board images. This includes a discussion of both the Black/White and laser profile imaging systems. The data collection boards that were used for transferring images from these imaging systems to computer memory are also described.
This thesis also describes the computer vision algorithms that were developed to extract defect information needed for making improved edging and trimming decisions. Some of the processing steps involved include background extraction, both global and local segmentation, connected component labeling and small area elimination and merging. Processing results obtained of green red oak samples show that both hardware and software of the prototype system seem to work well. However, since the program needed to actually create the edging and trimming solution based on defect information found by the computer vision system was not available it was impossible to quantitatively determine the value improvement to proposed system might offer.