Defect detection system for lumber
First Claim
1. Apparatus for inspecting lumber to determine the presence and location of defects to optimize cutting of the lumber, said apparatus comprising:
- first and second color cameras for imaging first and second faces of the lumber and generating color image data thereof;
optical scanning means for imaging the whole width of the lumber for generating image data of a profile of the lumber;
electro-magnetic scanning means for generating image data of internal features of the lumber;
means for generating histograms corresponding to the color image data and the internal features image data;
dynamic selecting means for dynamically selecting histogram threshold levels for each piece of lumber relative to a peak in each of the histograms representing clear wood;
data ordering means for analyzing the profile image data from said optical scanning means first, for analyzing the internal features image data from said electro-magnetic scanning means second to locate large defect regions, for analyzing the internal features image data from said electro-magnetic scanning means again to locate small defect regions, and for analyzing the color image data from said first and second cameras last; and
data removing means for finding regions in the image data that are known to be defect areas based on the histogram threshold levels selected by said dynamic selecting means and removing them from all image data and their corresponding histograms once found.
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Abstract
A machine vision system that can address a number of board, lineal, cant, and flitch inspection problems by incorporating all the sensors needed to address the surface feature detection problem, the three-dimensional shape detection problem, and the internal feature detection problem. To detect surface features, two color cameras are employed, one for imaging each of the major faces of a board, lineal, cant, or fitch. To address the three-dimensional shape detection problem, a high speed laser profiling device is employed. An x-ray scanning system is employed to detect internal features. The system is able to process material in a species-independent manner by using a histogram-based segmentation procedure for analyzing both the camera imagery and the x-ray imagery; and can detect small defects by removing the effects of large features from the histograms once they have been detected. The system also utilizes redundant information from the set of multiple sensors to improve system accuracy. The volume of data that must be analyzed due the use of three sets of sensors is reduced by ordering the way the data is analyzed. The laser profile data is processed first, followed by the x-ray data and the color imagery. Finally, the system reduces the required volume of data by incorporating a crack/check preserving filter. This filter is implemented in special purpose hardware, and filters the color imagery as it is collected.
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Citations
19 Claims
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1. Apparatus for inspecting lumber to determine the presence and location of defects to optimize cutting of the lumber, said apparatus comprising:
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first and second color cameras for imaging first and second faces of the lumber and generating color image data thereof; optical scanning means for imaging the whole width of the lumber for generating image data of a profile of the lumber; electro-magnetic scanning means for generating image data of internal features of the lumber; means for generating histograms corresponding to the color image data and the internal features image data; dynamic selecting means for dynamically selecting histogram threshold levels for each piece of lumber relative to a peak in each of the histograms representing clear wood; data ordering means for analyzing the profile image data from said optical scanning means first, for analyzing the internal features image data from said electro-magnetic scanning means second to locate large defect regions, for analyzing the internal features image data from said electro-magnetic scanning means again to locate small defect regions, and for analyzing the color image data from said first and second cameras last; and data removing means for finding regions in the image data that are known to be defect areas based on the histogram threshold levels selected by said dynamic selecting means and removing them from all image data and their corresponding histograms once found. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method for inspecting lumber to determine the presence and location of defects to optimize cutting of the lumber, said method comprising the steps of:
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(a) obtaining color image data for first and second faces of a piece of lumber; (b) obtaining profile image data for the whole width of the lumber; (c) obtaining image data of internal features of the lumber; (d) generating histograms corresponding to the color image data and the internal features data; (e) analyzing the profile image data using a computer processor; (f) dynamically selecting histogram threshold levels for the lumber relative to a peak representing clear wood in each of the histograms for the internal features image data; (g) following said steps (e) and (f), analyzing the internal features image data using the computer processor to locate large defect regions based on the analysis of the profile image data from said step (e) and based on the histogram threshold levels selected in said step (f) and removing the large defect regions so located from all the image data and their corresponding histograms; (h) following said step (g), dynamically selecting histogram threshold levels for the lumber relative to a peak representing clear wood in each of the histograms for the internal features image data as modified in said step (g); (i) following said step (h) analyzing the internal features image data again using the computer processor to locate small defect regions including finding regions, in the internal features image data that are known to be defect areas based on the histogram threshold levels selected in said step (h) and once found, removing them from all the image data and their corresponding histograms as modified in said step (g); (j) following said step (i), dynamically selecting histogram threshold levels for the lumber relative to a peak representing clear wood in each of the histograms for the color image data as modified in said step (i); (k) following said step (j), analyzing the color image data using the computer processor, including finding regions in the color image data that are known to be defect areas based on the histogram threshold levels for the color image data selected in said step (j); and (l) using the analysis of of said steps (e), (g), (i), and (k) to optimize cutting of the lumber. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15)
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16. Apparatus for inspecting a wood object to determine the presence and location of defects in the object, said apparatus comprising:
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means for generating color image data representing surface features of an object; means for generating profile image data representing the three-dimensional shape of the object; means for generating x-ray image data representing internal features of the object; first and second feature detecting computers to process the color, profile, and x-ray image data and locate defects, said first computer processing information obtained from a first surface of the object and said second computer processing information obtained from a second surface of the object, said first and second computers each including a memory; means for registering the color image data, the profile image data, and the x-ray image data by establishing a common coordinate system in two-dimensional space and by ensuring that the spatial resolution of each set of image data is the same; means for generating histograms corresponding to the color image data, the profile image data, and the x-ray image data; means for analyzing the color image data, the profile image data, and the x-ray image data using a histogram-based segmentation procedure to produce a respective segmented profile image, segmented color image, and segmented x-ray image; means for finding regions in each of the segmented profile, color, and x-ray images that are known to be defect areas by applying a connected component labeling algorithm to each of the segmented profile, color, and x-ray images and for removing the defect areas from each of the segmented profile, color, and x-ray images and its corresponding histogram, once found; means for ordering the analysis of the segmented profile, camera, and x-ray image data to decrease the volume of data that must be analyzed by;
first processing the profile data to locate areas of insufficient thickness, removing areas of insufficient thickness from the x-ray image data and then analyzing the remaining x-ray image data to locate areas of less density, removing areas of insufficient thickness and less density from the color image data and then analyzing the remaining color image data to confirm defects having both surface and internal features;image preprocessing means for preprocessing the image data from said means for generating color image data, said means for generating profile image data, and said means for generating x-ray image data, said preprocessing including synchronizing of the image data as it is collected; and interface means for transferring image data from each of said means for generating color image data, said means for generating profile image data, and said means for generating x-ray image data into computer memory in a standard direct memory access method so that no CPU intervention is required and for filtering the color image data as it is collected using a crack-preserving filter to reduce the resolution of the volume of the color image data while preserving the level of detail needed to locate and identify small cracks. - View Dependent Claims (17, 18, 19)
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Specification