Test of the Center for Automated Processing of Hardwoods' Auto-Image Detection and Computer-Based Grading and Cutup System
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Automated lumber grading and yield optimization using computer controlled saws will be plausible for hardwoods if and when lumber scanning systems can reliably identify all defects by type. Eisting computer programs could then be used to grade the lumber, identify the best cut-up solution, and control the sawing machines. The potential value of a scanning grading system depends on the accuracy and reliability of the computer-assigned grades compared to the performance of human graders. The potential worth of any scanning cut-up system is largely dependent on the parts recovered compared to today’s standard rough mill processing systems. The Center for Automated Processing of Hardwoods' (CAPH) scanning system tested is a color line-scan camera-based image processing system. We compared the system’s scanning-grader performance with the NHLA (National Hardwood Lumber Association) grades assigned by company graders. The scanning-grader results indicated that 20 of 50 company graded boards were graded too high and 5 too low. In total , 50 percent of the boards were manually misgraded. Initial results indicate that the CAPH color camera system missed small sections of some defects and misclassifyed some clear wood as defective.Keywords:
Grading (engineering)
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Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Erol Sarigul, A. Lynn Abbott, Daniel L. Schmoldt; Nondestructive rule-based defect detection and identification system in CT images of hardwood logs. AIP Conf. Proc. 30 April 2001; 557 (1): 1936–1943. https://doi.org/10.1063/1.1373989 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
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Automated lumber grading and yield optimization using computer controlled saws will be plausible for hardwoods if and when lumber scanning systems can reliably identify all defects by type. Existing computer programs could then be used to grade the lumber, identify the best cut-up solution, and control the sawing machines. The potential value of a scanning grading system depends on the accuracy and reliability of the computer-assigned grades compared to the performance of human graders. The potential worth of any scanning cut-up system is largely dependent on the parts recovered compared to today’s standard rough mill processing systems. The Center for Automated Processing of Hardwoods’ (CAPH) scanning system is a color line-scan camera-based image processing system. We compared the system’s scanning-grader performance with the NHLA grades assigned by company graders. The scanning-grader results indicated that 4 of 15 company graded boards were graded too high. In total, 67 percent of the boards were manually misgraded. Initial results indicate that the CAPH system is missing small sections of some defects and is misclassifying some clear wood as defective. We also compared the CAPH system’s scanning-optimization system to a rip-first rough mill processing system. The scanning-optimization results indicated a potential increase in rough part yield of 5 percent might be realized with the CAPH system.
Grading (engineering)
Grading scale
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This paper is concerned with scanning and assessment of hardwood lumber early in the manufacturing process. Scanning operations that take place immediately after the headrig have significantly greater potential to reduce loss and improve economic value, as compared to scanning that is performed during subsequent manufacturing steps. In spite of this, the scanning of green, unplaned lumber has received relatively little attention in the research community. Part of the reason for this is that image capture and analysis are more difficult when fibrous structures and debris are present near the surface of the wood. This paper describes a prototype system that addresses this problem. The system, which automatically provides an optimal edging and trimming solution along with resulting lumber grades, has been has been developed and tested for use with unplaned hardwood lumber that is still in the green state. The system obtains thickness (profile) and reflectance information at 1/16-inch (1.6-mm) resolution, using commercially available laser sources and a video camera. It analyzes the resulting images to detect wane and important lumber-degrading defects. Wane boundaries are detected with 3/16-inch (5-mm) error on average, and a modular artificial neural network is used to locate clear wood, knots, and decay. Using this surface information for each board, the system then automatically finds optimal solutions for placement of cuts to yield maximum commercial value based on current market prices.
Trimming
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Grading (engineering)
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Grading (engineering)
Slab
Machine Vision
Visual inspection
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Abstract In this article, we describe a system for machine vision–based lumber strength prediction. The system utilizes images taken from all four sides of pinewood boards. Those images are further divided into small subareas, and the local gradients inside each area are used to calculate the local grain direction. Together, these directions form the grain direction map. The grain direction map and knot features are used to predict the breaking strength of the board. Because of the high speed of production lines, we also present a parallel general-purpose graphics processing unit (GPGPU) implementation of the method to achieve real-time performance using low-cost hardware. We describe the challenges of the design on a GPU compared with a traditional central processing unit implementation. Most of the modern sawmills already have multiple camera systems in use, making the camera-based strength prediction extremely cost effective. In our experiments, an r 2 value of 0.63 was obtained between the measured strength attributes of the board and our strength prediction coefficient. The ground truth for the breaking strength was measured using destructive 3-point bending tests. Using a regular desktop computer, the described system achieves a throughput of over 50 Mpixels/s. For the parallel implementation, we provide qualitative evaluation of the results and a comparison of the computational speed on several platforms.
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Grading (engineering)
Laser Scanning
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In order to improve the elïïciency of apple's grading and ensure the scientilïcity and objectivity of grading, this paper presents a three-stage grading system, namely weighing grading, color grading and shape grading. Hardware design uses high-performance S3C2440 embedded controller, DSP controller, single-chip micro-processor controller, pressure load sensor, color recognition sensor, graphic recognition sensor and motor. Software design includes the realization of apple weighing DSP monitoring procedures, color grading SCM C51 procedures and shape grading embedded system procedures. Test results show that the maximum relative error of single stage is 5.6%; therefore system can fully meet the practical demands of apple grading.
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The initial breakdown of hardwood logs into lumber produces boards with rough (unplanned) surfaces. Because hardwood lumber value is determined by board size and by the percentage of defect-free area (lumber grade), sawmills remove some wood from the edges and/or ends of a board prior to sale. The overall goal of this research project is to develop a prototype scanning system that can automatically identify important defects (knots, wane, and voids) on rough hardwood lumber and can recommend optimal cuts to achieve maximum lumber value for each board. The first step for inspection system development is to integrate off-the-shelf scanning hardware components with in-house developed software to capture high-quality board images. Board illumination is realized by three, 650 mm lasers that cast lines across the width of a board as it moves longitudinally on a conveyor. A matrix array picture processor camera was selected for image capture. It contains an array of 256×256 photodiodes, where each row is addressable and can be manipulated by an on-chip processor. The camera performs initial processing locally, and transfers results to a host PC. A detailed description of instrumentation, system operation, and scanning capabilities are provided, which generate both profile and gray-scale images.
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For the inspection of wood, machine vision is the most common automated inspection method used at present. It is required to sort wood products by grade and to locate surface defects prior to cut-up. Many different sensing methods have been applied to inspection of wood including optical, ultrasonic, X-ray sensing in the wood industry. Nowadays the scanning system mainly employs CCD line-scan camera to meet the needs of accurate detection of lumber defects and real-time image processing. But this system needs exact feeding system and low deviation of lumber thickness. In this study low cost CCD area sensor was used for the development of image processing system for lumber being fed. When domestic red pine being fed on the conveyer belt, lumber images of irregular term of captured area were acquired because belt conveyor slipped between belt and roller. To overcome incorrect image merging by the unstable feeding speed of belt conveyor, it was applied template matching algorithm which was a measure of the similarity between the pattern of current image and the next one. Feeding the lumber over 13.8 m/min, general area sensor generates unreadable image pattern by the motion blur. The red channel of RGB filter showed a good performance for removing background of the green conveyor belt from merged image. Threshold value reduction method that was a image-based thresholding algorithm performed well for knot detection.
Conveyor belt
RGB color model
Machine Vision
Template matching
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