CN112129773A - Wood surface defect detection method, device, equipment, system and storage medium - Google Patents
Wood surface defect detection method, device, equipment, system and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a system for detecting surface defects of wood and a storage medium. Wherein, the method comprises the following steps: acquiring a laser line image of the conveyor belt under the action of a laser line generated by laser equipment; based on the obtained laser line image, extracting the laser line height position and the laser line width of the laser line; identifying whether wood exists on the conveyor belt or not based on the height position and/or the width of the laser line and a preset laser line model; and if wood exists on the conveyor belt, identifying the surface defects of the wood based on the laser line height position and the laser line width of the laser line. Because the surface of the wood has the tracheid effect, the surface defects of the wood can be accurately identified based on the height position and the width of the laser line, the defects of edge deletion, wormhole, dead knot, movable joint and the like can be accurately identified, and the identification requirement of quick identification can be met, so that the automatic sawing machine is suitable for the field of automatic sawing of the surface defects of the wood.
Description
Technical Field
The invention relates to the field of wood detection, in particular to a method, a device, equipment, a system and a storage medium for detecting surface defects of wood.
Background
The solid wood board is a decorative material formed by drying and processing natural wood, has the advantages of nature, beauty, safety, environmental protection, durability, electric heat insulation and the like, and is widely applied to furniture manufacturing, indoor and outdoor decoration and the like. In wood science and engineering, the quality grade of wood determines its usefulness in production applications. The surface defects of the wood, such as dead knots, movable joints, holes, wormholes and the like, not only affect the aesthetic property of the finished wood in the wood industry, but also affect the quality of the wood and the bearing capacity of the wood, so the detection and optimization of the surface defects of the wood are one of important processes in wood processing.
In the related art, the wood defect detection method is mainly based on X-ray, stress wave, ultrasonic wave, infrared, laser, optical camera and the like. In addition, a digital image processing technology, a computer vision technology and a pattern recognition technology can be applied to detection of wood defects, a fractal theory, wavelet multi-resolution analysis and an artificial neural network pattern recognition technology are combined, problems of texture segmentation, feature extraction, pattern recognition and the like of the wood surface defects are researched, and a novel detection method is formed.
With the rapid development of high-end solid wood processing machinery equipment, automatic identification of wood surface defects and automatic sawing are widely applied to the field of automation industry, but the problems of low precision or low speed and the like generally exist. In the related art, the detection and positioning of the defect area are realized by the difference of the physical characteristics of the normal area and the abnormal area on the surface of the wood, for example, the defect is detected by means of X-ray, infrared and the like, and the detection by the physical characteristics can meet the requirement of rapidity in industry, but has poor accuracy. In addition, an optical camera can be used for collecting pictures, a high-accuracy image processing algorithm is designed for detecting and positioning the defects on the surface of the wood, the high-accuracy image processing algorithm has the advantage of high accuracy, the requirement on industrial rapidity is often ignored, and the algorithm is large in calculation amount and long in time consumption.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, a device, a system and a storage medium for detecting defects on a wood surface, and aim to improve defect identification efficiency on the basis of satisfying defect identification accuracy.
The technical scheme of the embodiment of the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides a method for detecting surface defects of wood, including:
acquiring a laser line image of a conveyor belt under the action of a laser line generated by laser equipment, wherein the laser line extends along the width direction of the conveyor belt on a horizontal plane;
based on the obtained laser line image, extracting the laser line height position and the laser line width of the laser line;
identifying whether wood exists on the conveyor belt or not based on the laser line height position and/or the laser line width of the laser line and a preset laser line model;
and if wood exists on the conveyor belt, identifying the surface defects of the wood based on the laser line height position and the laser line width of the laser line.
In some embodiments, the identifying whether wood is present on the conveyor belt based on the laser line height position and/or the laser line width of the laser line and a preset laser line model comprises:
comparing the height position and the width of the laser line with a preset first laser line model and a preset second laser line model to obtain a first similarity and a second similarity, wherein the first similarity represents the similarity between the laser line and the first laser line model, and the second similarity represents the similarity between the laser line and the second laser line model;
determining that the first similarity is greater than the second similarity, and judging that wood exists on the conveyor belt; alternatively, the first and second electrodes may be,
determining that the laser line width of the laser line exceeds the scattering width value of the laser line in the second laser line model and reaches a first set threshold value, and judging that wood exists on the conveyor belt; alternatively, the first and second electrodes may be,
determining that the height position of the laser line exceeds the height position of the laser line in the second laser line model and reaches a second set threshold value, and judging that wood exists on the conveyor belt;
wherein the first laser line model is used for representing the height position and the scattering width value of the laser line acting on the surface of the wood on the conveyor belt, and the second laser line model is used for representing the height position and the scattering width value of the laser line acting on the surface of the conveyor belt.
In some embodiments, the method further comprises:
acquiring a first laser line image of the surface of the wood on the conveyor belt under the action of a laser line generated by the laser equipment, and constructing a first laser line model based on the first laser line image;
and acquiring a second laser line image of the surface of the conveyor belt under the action of the laser line generated by the laser equipment, and constructing a second laser line model based on the second laser line image.
In some embodiments, the identifying the surface defects of the wood based on the laser line height position and the laser line width of the laser line comprises:
constructing a relative height image of the wood based on a difference between a laser line height position of the laser line and a height position in the first laser line model;
constructing a relative scattering image of the wood based on a difference value between the laser line width of the laser line and the scattering width value in the first laser line model;
determining whether the wood has a first defect based on the relative height image;
determining whether a second defect exists in the wood based on the relative scatter image;
wherein the first defect comprises at least one of: edge deletion and moth eye; the second defect includes at least one of: a dead knot and a movable joint.
In some embodiments, said constructing said first laser line model based on said first laser line image comprises:
acquiring a plurality of frames of the first laser line images;
determining the height position of the laser line on the wood based on the plurality of frames of the first laser line images;
determining a scattering width value of a laser line on the wood based on the plurality of frames of the first laser line images;
the constructing the second laser line model based on the second laser line image comprises:
acquiring a plurality of frames of the second laser line images;
determining the height position of the laser line on the conveyor belt based on the plurality of frames of the second laser line images;
and determining the scattering width value of the laser line on the conveyor belt based on the plurality of frames of the second laser line images.
In some embodiments, said constructing a relative height image of said wood based on the difference of the laser line height position of said laser line and the height position in said first laser line model comprises:
and constructing a relative height image on the corresponding area of the wood aiming at the acquired multiple frames of laser line images, wherein the value of each pixel point in the relative height image is determined based on the difference between the height position of the laser line and the height position in the first laser line model.
In some embodiments, constructing a relative scatter image of the wood based on a difference in laser line width of the laser line and a scatter width value in the first laser line model comprises:
and constructing a relative scattering image on the corresponding area of the wood aiming at the acquired multiple frames of laser line images, wherein the value of each pixel point in the relative scattering image is determined based on the difference value between the laser line width and the scattering width value in the first laser line model.
In some embodiments, the method further comprises:
if the first defect exists, determining a defect position corresponding to the first defect based on the relative height image; and/or the presence of a gas in the gas,
and if the second defect exists, determining the defect position corresponding to the second defect based on the relative scattering image.
In a second aspect, an embodiment of the present invention further provides a device for detecting surface defects of wood, including:
the device comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring a laser line image of a conveyor belt under the action of a laser line generated by laser equipment, and the laser line extends along the width direction of the conveyor belt on a horizontal plane;
the characteristic extraction module is used for extracting the laser line height position and the laser line width of the laser line based on the acquired laser line image;
the first identification module is used for identifying whether wood exists on the conveyor belt or not based on the laser line height position and/or the laser line width of the laser line and a preset laser line model;
and the second identification module is used for identifying the surface defects of the wood based on the laser line height position and the laser line width of the laser line if the wood exists on the conveyor belt.
In a third aspect, an embodiment of the present invention further provides a wood surface defect detecting apparatus, including: a processor and a memory for storing a computer program capable of running on the processor, wherein the processor, when running the computer program, is configured to perform the steps of the method according to an embodiment of the invention.
In a fourth aspect, an embodiment of the present invention further provides a wood surface defect detection system, including:
the conveying belt is used for conveying the wood on line;
a laser device for generating a laser line acting on the wood;
the image acquisition equipment is used for acquiring a laser line image;
the wood surface defect detection equipment provided by the embodiment of the invention is connected with the image acquisition equipment and is used for receiving the laser line image acquired by the image acquisition equipment.
In a fifth aspect, an embodiment of the present invention further provides a storage medium, where a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the method according to the embodiment of the present invention are implemented.
According to the technical scheme provided by the embodiment of the invention, a laser line image of a conveyor belt under the action of a laser line generated by laser equipment is obtained, the laser line height position and the laser line width of the laser line in the laser line image are extracted, whether wood exists on the conveyor belt is identified based on the laser line height position and/or the laser line width of the laser line and a preset laser line model, and if wood exists on the conveyor belt, the surface defect of the wood is identified based on the laser line height position and the laser line width of the laser line; because the surface of the wood has the tracheid effect, the surface defects of the wood can be accurately identified based on the height position and the width of the laser line, the defects of edge deletion, wormhole, dead knot, movable joint and the like can be accurately identified, and the identification requirement of quick identification can be met, so that the automatic sawing machine is suitable for the field of automatic sawing of the surface defects of the wood.
Drawings
FIG. 1 is a schematic flow chart of a method for detecting surface defects of wood according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a wood surface defect detection system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the principle of real-scale coincidence between a reconstructed image and wood in an embodiment of the present invention;
FIG. 4 is a schematic diagram of the laser beam's tracheid effect in the normal area of the wood surface according to the embodiment of the present invention;
FIG. 5 is a schematic view of a laser line image when the laser line scans the wood chipped edge according to an embodiment of the present invention;
FIG. 6 is a schematic view of a laser line image of a laser line in a wood segment area according to an embodiment of the present invention;
FIG. 7 is a schematic structural diagram of a wood surface defect detecting apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a wood surface defect detecting apparatus according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Before describing the method for detecting surface defects of wood according to the embodiment of the present invention, the tracheid effect related to the embodiment of the present invention is explained. The embodiment of the invention detects the surface defects of the wood based on the tracheid effect of the laser line on the surface of the wood. The tracheid effect is an observable optical phenomenon, when laser lines are projected on the surface of wood, a part of light directly irradiates on the surface, and a part of light penetrates through the surface and is conducted in the wood made of semi-mirror materials. Softwood fibers (i.e., tracheids) are better able to transmit light, while defects such as wood knots are not as capable of transmitting light as normal areas. The embodiment of the invention utilizes the characteristic, and detects the wood defect area by combining the laser equipment and the image detection equipment (such as an array camera), thereby improving the accuracy and rapidity of the detection of the wood surface defects and having great significance for industrial application.
The embodiment of the invention provides a method for detecting surface defects of wood, which comprises the following steps of:
illustratively, in the embodiment of the present invention, a laser device and an image capturing device may be disposed near the conveyor belt, the image capturing device is connected to the wood surface defect detecting device, and the wood surface defect detecting device implements the wood surface defect detecting method according to the embodiment of the present invention.
As shown in fig. 2, in one application example, a wood surface defect detecting system includes: the device comprises a conveyor belt 201, a laser device 202, an image acquisition device 203 and a wood surface defect detection device 204. Wherein the laser device 202 may be a line laser, the image acquisition device 203 may be an array camera, and the wood surface defect detection device 204 may be a processing device with image data processing capability. The line laser may be positioned at the lower left of the array camera shown in fig. 2, above the conveyor belt, and inclined at an angle of about 60 ° to the horizontal plane; the array camera is positioned at a position which is half a meter above the conveyor belt, and the visual field range of the array camera can cover the width direction of the whole conveyor belt so as to ensure that the wood on the conveyor belt can be completely shot.
In practical application, the AOI (Automated Optical Inspection) parameter of the image acquisition device 203 may be set, so that the resolution of the image is as low as possible under the condition that the laser line can be completely shot, that is, the image is as small as possible, and it is ensured that the calculated amount is smaller and the calculation speed is faster when image processing and data analysis are performed on each frame of image.
In the embodiment of the present invention, the direction of the laser line generated by the laser device 202 is perpendicular to the longitudinal direction (i.e., the growth direction) of the wood on the horizontal plane, and the image acquisition device 203 acquires the laser line image based on the set camera frame rate and transmits the laser line image to the wood surface defect detection device 204, so that the wood surface defect detection device 204 can acquire the corresponding laser line image in the working process of the conveyor belt on line in real time.
102, extracting the height position and the width of the laser line based on the acquired laser line image;
here, the wood surface defect detecting apparatus 204 performs feature extraction corresponding to the acquired laser line image, and extracts the laser line height position and the laser line width of the laser line.
103, identifying whether wood exists on the conveyor belt or not based on the laser line height position and/or the laser line width of the laser line and a preset laser line model;
here, the wood surface defect detecting apparatus 204 may judge the laser line height position and/or the laser line width of the extracted laser line using a laser line model constructed in advance, recognize whether wood exists on the conveyor belt, and perform step 104 after determining that wood exists on the conveyor belt.
And 104, if wood exists on the conveyor belt, identifying the surface defects of the wood based on the laser line height position and the laser line width of the laser line.
Here, the wood surface defect detecting device 204 may reconstruct a relative height image representing the wood surface height information and a relative scattering image representing the wood surface structure information by using a plurality of frames of laser line images acquired on line based on the laser line height position and the laser line width of the laser line extracted from each frame of laser line image, thereby realizing the detection and the positioning of the surface defect. Because the surface of the wood has the tracheid effect, the surface defects of the wood can be accurately identified based on the height position and the width of the laser line, the defects of edge deletion, wormhole, dead knot, movable joint and the like can be accurately identified, and the identification requirement of quick identification can be met, so that the automatic sawing machine is suitable for the field of automatic sawing of the surface defects of the wood.
In practical application, the extracted laser line height position and laser line width of the laser line need to be used for constructing a relative height image representing the wood surface height information and a relative scattering image representing the wood surface structure information, so that the detection and the positioning of the surface defects are realized. In practical industrial sawing tasks, it is necessary to provide exact position coordinates for the sawing device, so that the length-width ratio of the reconstructed map must be consistent with the wood material object. In the process of reconstructing the relative scattering map and the relative height map of the wood, the pixel value of each row of pixel points of the reconstructed image is assigned by the information contained in each frame of laser line image, namely, one or more rows of wood reconstructed images can be reconstructed from one frame of laser line image. When a line of pixel values are reconstructed in a frame of laser line image, the resolution of the reconstructed image is highest; when a plurality of lines of images are reconstructed by one frame of laser line image information, the resolution of the reconstructed images is lowered. When the wood is conveyed on the conveyor belt, the wood passes through the image acquisition equipment at a certain speed, the image acquisition equipment shoots at a certain frame rate and stores the shot image in the memory of the computer, and the computer processes the stored image by using another thread. When the shooting frame rate is fixed, the faster the speed of the conveyor belt is, the shorter the time for the wood to pass through the visual field range of the image acquisition equipment is, and the number of the shooting frames is relatively less; the slower the conveyor belt speed, the longer the wood passes through the field of view of the image acquisition device, and the greater the number of frames taken. The resolution of the reconstructed image is related to the number of lines (image reconstruction step size) of the reconstructed image corresponding to one frame of image, the speed of the conveyor belt and the shooting frame rate which are set artificially.
Based on this, in order to satisfy that the ratio of the width and the height of the image in the relative height image and the relative scattering image is consistent with the ratio of the width and the height of the wood real object, and the resolution of the reconstructed image is reasonable, it is necessary to reasonably set the speed of the conveyor belt 201, the shooting frame rate of the image acquisition device 203, and the like.
Exemplarily, as shown in fig. 3, l and h are width and height of the wood real object, respectively, and x and y are width and height of the wood part in the reconstructed image, respectively, and keeping the ratio of the reconstructed image to the wood real object consistent can obtain:
the height value y of the reconstructed image is determined by a line laser image under the visual field of the image acquisition equipment, the line laser has obvious difference between the wood and a red scattering field excited on the conveyor belt, the laser line in the image is found in the partial area of the wood through comparison, and the wood height y in the visual field range is calculated through data. From the above, the height value y of the reconstructed wood image is only related to the wood physical height value h and the visual field range of the image acquisition equipment. The reconstructed image width x is related to the number of lines (reconstruction step length) of a corresponding reconstructed image of a preset frame of image and the total number of frames of images acquired by the image acquisition equipment in the process that wood passes through the shooting range of the image acquisition equipment, namely:
x＝d·λ
wherein d is an image reconstruction step length (i.e. the number of lines of a reconstructed image corresponding to one frame of image), and λ is the total number of frames of laser line images acquired during the process that the wood to be detected passes through the visual field of the image acquisition equipment at a constant speed. The following relationship is provided between λ and the shooting frame rate and the conveyor belt rate of the image acquisition device:
wherein l is the wood material object width, v is the conveying belt speed, fpsThe shooting frame rate of the image acquisition equipment.
Finally, the following can be obtained:
therefore, the actual height h of a piece of wood and the reconstructed image height y determined by the visual field range of the camera can be measured, and the length-width ratio of the reconstructed image is controlled to be consistent with the actual length-width ratio of the wood by adjusting the shooting frame rate of the image acquisition device, the line number (step length) of the reconstructed image corresponding to one frame of image and the speed of the conveyor belt, so that accurate defect coordinates can be provided for the wood sawing device subsequently.
In some embodiments, the pre-constructed laser line model comprises: a first laser line pattern and a second laser line pattern. Wherein the first laser line model represents the height position and the scattering width value of the laser line acting on the surface of the wood on the conveyor belt, and the second laser line model represents the height position and the scattering width value of the laser line acting on the surface of the conveyor belt.
In some embodiments, pre-constructing the laser line model comprises:
acquiring a first laser line image of the surface of the wood on the conveyor belt under the action of a laser line generated by the laser equipment, and constructing a first laser line model based on the first laser line image;
and acquiring a second laser line image of the surface of the conveyor belt under the action of the laser line generated by the laser equipment, and constructing a second laser line model based on the second laser line image.
In some embodiments, said constructing said first laser line model based on said first laser line image comprises:
acquiring a plurality of frames of the first laser line images;
determining the height position of the laser line on the wood based on the plurality of frames of the first laser line images;
determining a scattering width value of a laser line on the wood based on the plurality of frames of the first laser line images;
the constructing the second laser line model based on the second laser line image comprises:
acquiring a plurality of frames of the second laser line images;
determining the height position of the laser line on the conveyor belt based on the plurality of frames of the second laser line images;
and determining the scattering width value of the laser line on the conveyor belt based on the plurality of frames of the second laser line images.
Illustratively, a standard piece of wood (i.e., a piece of wood without any defects) may be placed on the conveyor belt, and the first laser line pattern may be constructed using the first laser line image acquired under this scenario. FIG. 4 is a schematic diagram showing the laser beam's tracheid effect in the normal area of the wood surface. The laser line is normally scattered in the normal area of the wood to form light spots which are bright in the middle and gradually scattered to two sides. The central brightest laser line portion information is available in the green channel of the image, while the red portion scattered outward due to the tracheid effect is located in the red channel of the image.
Here, the grayscale image imgR is constructed based on a red channel of the first laser line image, the grayscale image imgG is constructed based on a green channel of the first laser line image, a scattering width model of the first laser line model may be constructed based on the grayscale image imgR, and a height model of the first laser line model may be constructed based on the grayscale image imgG.
Illustratively, a gaussian distribution model can be used to model the distribution of the red channel gray values in the laser line image:
wherein x is the gray value of the pixel point of the imgR image, f (x) is the probability that the gray value belongs to the bright spot and the scattering area of the laser line, and mu and sigma are the average value and the variance of the gray value of the pixel point in the scattering area of the laser line in the imgR images respectively, and a gray value distribution model of the red channel of the laser line is established.
Based on the gray value distribution model of the red channel in the first laser line image, if the gray probability f (x) of a certain pixel point is greater than a certain threshold value Th, the pixel point belongs to a laser line scattering area (namely, the pixel point belonging to an area with a brighter laser line center in a given threshold value range is counted), wherein the probability threshold value Th range is the interval [0.5,0.8 ]. And carrying out classified statistics on each pixel point in the region, taking a column of a frame of image as a unit to count the sum of the number of the pixel points belonging to the laser line scattering region, and taking the sum as the width value of the laser line. For example, the average value of the laser line width values in the N first laser line images is taken as the scattering width value of the first laser line model.
Illustratively, a gray-scale image imgG may be constructed based on the green channel in the first laser line image, and a fixed threshold value may be set to binarize imgG, and the threshold value range may be set to the value of the [180,230] interval. And counting laser line pixel points which are larger than the threshold value by taking the rows as units for each frame of image, calculating the height position coordinate of the center, and taking the coordinate as the height coordinate value of the center position of the laser line. And taking N second laser line images, and performing statistical calculation on the height position mean value of the laser line center to serve as the height position of the first laser line model. Wherein, N is a natural number greater than 1, and can be reasonably selected based on the image resolution.
It can be understood that the process of constructing the second laser line model is similar to the process of constructing the first laser line model, except that a second laser line image acquired in a scene where no wood is placed on the conveyor belt is needed, and the second laser line model is constructed based on the second laser line image.
In some embodiments, the difference between the height position of the first laser line pattern and the height position of the second laser line pattern may also be determined as the standard deviation of the laser line acting between the wood surface and the conveyor surface.
In some embodiments, the identifying whether wood is present on the conveyor belt based on the laser line height position and/or the laser line width of the laser line and a preset laser line model comprises:
comparing the height position and the width of the laser line with a preset first laser line model and a preset second laser line model to obtain a first similarity and a second similarity, wherein the first similarity represents the similarity between the laser line and the first laser line model, and the second similarity represents the similarity between the laser line and the second laser line model;
and determining that the first similarity is larger than the second similarity, and judging that wood exists on the conveyor belt.
In some embodiments, the identifying whether wood is present on the conveyor belt based on the laser line height position and/or the laser line width of the laser line and a preset laser line model comprises:
and determining that the laser line width of the laser line exceeds the scattering width value of the laser line in the second laser line model and reaches a first set threshold value, and judging that wood exists on the conveyor belt.
For example, several columns of each frame of laser line image may be extracted, the laser line width of the extracted columns may be calculated one by one and compared with the scattering width value of the second laser line model, so as to determine whether wood exists, for example, if 5 columns are uniformly taken from left to right, the laser line width may be calculated, and if the laser line width is greater than 1.5 times the scattering width value of the second laser line model, wood may be considered to exist.
In some embodiments, the identifying whether wood is present on the conveyor belt based on the laser line height position and/or the laser line width of the laser line and a preset laser line model comprises:
and determining that the height position of the laser line exceeds the height position of the surface of the conveyor belt in the second laser line model and reaches a second set threshold value, and judging that wood exists on the conveyor belt.
For example, several columns of each laser line image may be extracted, for example, 5 columns are uniformly extracted from left to right, an average value of the height positions of the centers of the extracted laser lines is calculated, the average value of the height positions is compared with the height position of the surface of the conveyor belt in the second laser line model to determine whether wood exists, if the difference value between the average value of the height positions of the extracted laser lines and the height position of the surface of the conveyor belt in the second laser line model approaches to 0, it is determined that wood does not exist, otherwise, it is determined that wood exists.
In some embodiments, said constructing a relative height image of said wood based on the difference of the laser line height position of said laser line and the height position in said first laser line model comprises:
and constructing a relative height image on the area corresponding to the wood aiming at the acquired multiple frames of laser line images (namely, the multiple frames of laser line images acquired in real time in the working process of the conveyor belt), wherein the value of each pixel point in the relative height image is determined based on the difference between the height position of the laser line and the height position in the first laser line model.
Illustratively, fig. 5 is a schematic view of a laser line image of a laser line scanning a wood chipped area, wherein the laser line is offset from a normal area of the wood when the wood chipped area is scanned. Based on the first laser line model, performing threshold segmentation on pixel points in the image green channel gray level image of each frame of laser line to obtain a binary image of the light spot position of the green channel laser line, and performing threshold segmentation on the pixels in the green channel gray level image of each frame of laser imagePerforming threshold segmentation on the points to obtain a binary image of the light spot position of the green channel laser line, counting the height position of the center of each line of laser lines in the binary image to obtain the deviation information of the height position of each line of laser lines in each frame of image and the standard laser lineiAnd i is the number of laser line image columns per frame. And reconstructing the wood image by taking the standard deviation of the laser line at the positions of the wood and the conveyor belt as a standard value. If the resolution of the image is set to be H multiplied by width, the pixel value vector can be calculated by each frame of laser image
The pixel value vector extracted from each frame of laser image has width elements, and each element is mapped to 0, 255 in a standardized way]And assigning values to width pixel points of one or more lines corresponding to the reconstructed relative height map in sequence, and sequentially extracting pixel value vectors from a plurality of frames of laser images and assigning values to the reconstructed images in sequence to reconstruct the relative height map containing relative height information. Obviously, when the laser scans the edge-lacking area, the center position of the laser line is closer to the standard position of the laser line on the conveyor belt, the deviation value is smaller,i<, appear darker in the reconstructed image.
Here, it may be determined whether the wood has the first defect based on the relative height image; wherein the first defect comprises at least one of: lack of edges and wormholes.
In some embodiments, constructing a relative scatter image of the wood based on a difference in laser line width of the laser line and a scatter width value in the first laser line model comprises:
and constructing a relative scattering image on a region corresponding to the wood aiming at the acquired multiple frames of laser line images (namely, the multiple frames of laser line images acquired in real time in the working process of the conveyor belt), wherein the value of each pixel point in the relative scattering image is determined based on the difference value between the laser line width and the scattering width value in the first laser line model.
FIG. 6 is a schematic view of a laser line image of a laser line in a wood segment area, in a wood defect areaThe scattering effect produced by the laser lineblast effect is significantly different. In the nodal region, the laser scattering ability is obviously weaker than that in the normal region, the region where the laser bright spots are scattered to two sides becomes smaller, and the whole laser line width becomes narrow. Classifying pixel points in a red channel gray level image in each frame of laser line image based on the first laser line model to obtain a red channel laser line width binary image, classifying pixel points in the red channel gray level image in each frame of laser image to obtain a red channel laser line width binary image, counting the width of each row of laser lines in the binary image to obtain the width information omega of each row of laser lines in each frame of imageiAnd i is the number of laser line image columns per frame. Laser line standard line width omega in laser line scattering areauAnd reconstructing the wood image as a standard value. If the resolution of the image is set to be H multiplied by width, the pixel value vector can be calculated in each frame of laser image
The pixel value vector extracted from each frame of laser image has width elements, and each element is mapped to 0, 255 in a standardized way]And assigning values to one or more lines of width pixel points corresponding to the reconstructed scattergram, and sequentially extracting pixel value vectors from a plurality of frames of laser images and assigning values to the reconstructed images to reconstruct the wood scattergram containing the prior information of the wood defects. It is apparent that when the laser scans the nub or other defect area, the laser linewidth ω < ωμThe pixel value is small, and the reconstructed image is black; when the laser line width omega is larger than omegaμAnd meanwhile, the laser line is considered to be in a normal area, and the maximum value 255 of the standardized mapping interval is assigned to the corresponding pixel point of the reconstructed image.
Here, it may be determined whether the wood has the second defect based on the relative scattering image; the second defect includes at least one of: a dead knot and a movable joint.
In some embodiments, the method further comprises:
if the first defect exists, determining a defect position corresponding to the first defect based on the relative height image; and/or the presence of a gas in the gas,
and if the second defect exists, determining the defect position corresponding to the second defect based on the relative scattering image.
In the embodiment of the invention, as the relative height image and the ratio of the width to the height in the relative scattering image are consistent with the ratio of the width to the height of the wood real object, the defect position can be determined on the relative height image or the relative scattering image and converted to the corresponding position on the wood real object, so that the defects on the wood can be conveniently sawed on line.
Exemplarily, first, it is determined by reconstructing the relative height image that wood chipping, wormholes, etc. cause unevenness of the wood surface to generate defects having a certain height difference from the normal area, such defects having a distinct feature on the relative height image. For example, setting a threshold value to carry out binarization processing on the relative height map, wherein the threshold value range is between [30 and 50], the larger the actual height value of a region with lower gray value on the relative height map is relative to the normal wood height deviation value, namely the larger the probability that the region has defects such as edge deletion, wormholes and the like is, setting a reasonable threshold value, processing the defects such as edge deletion, wormholes and the like on the relative height map, and taking the relative height binary map result after threshold segmentation as a wood edge deletion positioning result; then, other defects such as dead knots, joints and the like are positioned by reconstructing a relative scatter diagram of the wood. The scatter diagram does not have defects with height difference with normal areas, so that the scatter diagram has complementary effect with a relative height diagram, a threshold value is set to carry out binarization processing on the scatter diagram, the threshold value range can be between [30,50], and the result of the scatter binary diagram after threshold value segmentation can be used as the positioning result of other defects of the wood.
From the above description, it can be known that, in the embodiments of the present invention, the method of combining machine vision and laser line scanning improves the accuracy and speed of identifying the surface defects of the wood, and provides accurate information of the defect positions for the wood sawing equipment. Obtaining a relative scattering image and a relative height image of the wood containing the surface defect information of the wood in a line laser scanning mode, wherein the relative scattering image mainly reflects the surface dead knot, the movable joint and other structural information of defect types which cause the difference between the material of the defects and the normal plates; the relative height image is insensitive to most of defect types which do not influence the flatness of the board, and can mainly assist in judging defects such as wood edge deletion, wormholes and the like. In practical application, firstly defects such as wood edge deletion and the like are positioned through the reconstructed relative height map, then a connected domain of the defect part of the relative scattering image is segmented by adopting a threshold value (or other classification methods), and as the reconstructed relative scattering image filters a large amount of surface information of the board such as textures and colors and the like and only required defect information is left, a good segmentation effect can be achieved on the defect region by a simple image segmentation method. And combining the two pieces of reconstructed image information to complete the positioning of the surface defects of the wood.
In order to implement the method of the embodiment of the present invention, an embodiment of the present invention further provides a device for detecting a wood surface defect, where the device for detecting a wood surface defect corresponds to the method for detecting a wood surface defect, and each step in the method for detecting a wood surface defect is also completely applicable to the embodiment of the device for detecting a wood surface defect.
As shown in fig. 7, the apparatus 700 for detecting surface defects of wood includes: the system comprises an acquisition module 701, a feature extraction module 702, a first recognition module 703 and a second recognition module 704, wherein the acquisition module 701 is used for acquiring a laser line image of a conveyor belt under the action of a laser line generated by laser equipment, and the laser line extends along the width direction of the conveyor belt on a horizontal plane; the feature extraction module 702 is configured to extract a laser line height position and a laser line width of the laser line based on the acquired laser line image; the first identification module 703 is configured to identify whether wood exists on the conveyor belt based on the laser line height position and/or the laser line width of the laser line and a preset laser line model; the second identification module 704 is configured to identify surface defects of wood based on a laser line height position and a laser line width of the laser line if wood is present on the conveyor belt.
In some embodiments, the first identifying module 703 is specifically configured to:
comparing the height position and the width of the laser line with a preset first laser line model and a preset second laser line model to obtain a first similarity and a second similarity, wherein the first similarity represents the similarity between the laser line and the first laser line model, and the second similarity represents the similarity between the laser line and the second laser line model;
determining that the first similarity is greater than the second similarity, and judging that wood exists on the conveyor belt; alternatively, the first and second electrodes may be,
determining that the laser line width of the laser line exceeds the scattering width value of the laser line in the second laser line model and reaches a first set threshold value, and judging that wood exists on the conveyor belt; alternatively, the first and second electrodes may be,
determining that the height position of the laser line exceeds the height position of the surface of the conveyor belt in the second laser line model and reaches a second set threshold value, and judging that wood exists on the conveyor belt;
wherein the first laser line model represents a height position and a scattering width value of a laser line acting on the surface of the wood, and the second laser line model represents a height position and a scattering width value of a laser line acting on the surface of the conveyor belt.
In some embodiments, further comprising: a building module 705, configured to obtain a first laser line image of the surface of the wood on the conveyor belt under the action of the laser line generated by the laser device, and build the first laser line model based on the first laser line image; and acquiring a second laser line image of the surface of the conveyor belt under the action of the laser line generated by the laser equipment, and constructing a second laser line model based on the second laser line image.
In some embodiments, the second identification module 704 is specifically configured to:
constructing a relative height image of the wood based on a difference between a laser line height position of the laser line and a height position in the first laser line model;
constructing a relative scattering image of the wood based on a difference value between the laser line width of the laser line and the scattering width value in the first laser line model;
determining whether the wood has a first defect based on the relative height image;
determining whether a second defect exists in the wood based on the relative scatter image;
wherein the first defect comprises at least one of: edge deletion and moth eye; the second defect includes at least one of: a dead knot and a movable joint.
In some embodiments, the construction module 705 constructs the first laser line model based on the first laser line image, including:
acquiring a plurality of frames of the first laser line images;
determining the height position of the laser line on the wood based on the plurality of frames of the first laser line images;
determining a scattering width value of a laser line on the wood based on the plurality of frames of the first laser line images;
the building module 705 builds the second laser line model based on the second laser line image, including:
acquiring a plurality of frames of the second laser line images;
determining the height position of the laser line on the conveyor belt based on the plurality of frames of the second laser line images;
and determining the scattering width value of the laser line on the conveyor belt based on the plurality of frames of the second laser line images.
In some embodiments, the second identification module 704 is specifically configured to:
and constructing a relative height image on the corresponding area of the wood aiming at the acquired multiple frames of laser line images, wherein the value of each pixel point in the relative height image is determined based on the difference between the height position of the laser line and the height position in the first laser line model.
In some embodiments, the second identification module 704 is specifically configured to:
and constructing a relative height image on the corresponding area of the wood aiming at the acquired multiple frames of laser line images, wherein the value of each pixel point in the relative height image is determined based on the difference between the height position of the laser line and the height position in the first laser line model.
In some embodiments, the second identification module 704 is further configured to:
if the first defect exists, determining a defect position corresponding to the first defect based on the relative height image; and/or the presence of a gas in the gas,
and if the second defect exists, determining the defect position corresponding to the second defect based on the relative scattering image.
In practical application, the obtaining module 701, the feature extracting module 702, the first identifying module 703, the second identifying module 704 and the constructing module 705 may be implemented by a processor in the wood surface defect detecting apparatus. Of course, the processor needs to run a computer program in memory to implement its functions.
It should be noted that: in the wood surface defect detecting device provided in the above embodiment, when detecting the wood surface defect, only the division of the program modules is taken as an example, and in practical applications, the processing distribution may be completed by different program modules according to needs, that is, the internal structure of the device is divided into different program modules to complete all or part of the processing described above. In addition, the wood surface defect detection device provided by the above embodiment and the wood surface defect detection method embodiment belong to the same concept, and the specific implementation process thereof is described in the method embodiment and is not described herein again.
Based on the hardware implementation of the program module, in order to implement the method according to the embodiment of the present invention, an embodiment of the present invention further provides a wood surface defect detecting apparatus. Fig. 8 shows only an exemplary structure of the apparatus for detecting surface defects of wood, not the entire structure, and a part or the entire structure shown in fig. 8 may be implemented as necessary.
As shown in fig. 8, an embodiment of the present invention provides a wood surface defect detecting apparatus 800 including: at least one processor 801, memory 802, a user interface 803, and at least one network interface 804. The various components of the wood surface defect detection apparatus 800 are coupled together by a bus system 805. It will be appreciated that the bus system 805 is used to enable communications among the components of the connection. The bus system 805 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as bus system 805 in fig. 8.
The user interface 803 may include, among other things, a display, a keyboard, a mouse, a trackball, a click wheel, a key, a button, a touch pad, or a touch screen.
The memory 802 in the embodiment of the present invention is used to store various types of data to support the operation of the wood surface defect detecting apparatus. Examples of such data include: any computer program for operating on a wood surface defect detecting apparatus.
The method for detecting the surface defects of the wood disclosed by the embodiment of the invention can be applied to the processor 801 or realized by the processor 801. The processor 801 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the wood surface defect detection method may be implemented by hardware integrated logic circuits or instructions in software form in the processor 801. The Processor 801 may be a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Processor 801 may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed by the embodiment of the invention can be directly implemented by a hardware decoding processor, or can be implemented by combining hardware and software modules in the decoding processor. The software module may be located in a storage medium located in the memory 802, and the processor 801 reads the information in the memory 802, and completes the steps of the wood surface defect detection method provided by the embodiment of the present invention in combination with the hardware thereof.
In an exemplary embodiment, the wood surface defect detecting Device may be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), FPGAs, general purpose processors, controllers, Micro Controllers (MCUs), microprocessors (microprocessors), or other electronic components for performing the aforementioned methods.
It will be appreciated that the memory 802 can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The described memory for embodiments of the present invention is intended to comprise, without being limited to, these and any other suitable types of memory.
In some embodiments, an embodiment of the present invention further provides a wood surface defect detecting system, as shown in fig. 2, wherein the wood surface defect detecting device 204 may be the wood surface defect detecting device 800, and the specific defect detecting method refers to the foregoing description, and is not described herein again.
In an exemplary embodiment, the embodiment of the present invention further provides a storage medium, specifically a computer storage medium, which may be a computer readable storage medium, for example, a memory 802 storing a computer program, where the computer program is executable by a processor 801 of a wood surface defect detecting apparatus to complete the steps described in the method of the embodiment of the present invention. The computer readable storage medium may be a ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface Memory, optical disk, or CD-ROM, among others.
It should be noted that: "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
In addition, the technical solutions described in the embodiments of the present invention may be arbitrarily combined without conflict.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (12)
1. A method for detecting surface defects of wood is characterized by comprising the following steps:
acquiring a laser line image of a conveyor belt under the action of a laser line generated by laser equipment, wherein the laser line extends along the width direction of the conveyor belt on a horizontal plane;
based on the obtained laser line image, extracting the laser line height position and the laser line width of the laser line;
identifying whether wood exists on the conveyor belt or not based on the laser line height position and/or the laser line width of the laser line and a preset laser line model;
and if wood exists on the conveyor belt, identifying the surface defects of the wood based on the laser line height position and the laser line width of the laser line.
2. The method of claim 1, wherein identifying whether wood is present on the conveyor belt based on the laser line height position and/or laser line width of the laser line and a preset laser line pattern comprises:
comparing the height position and the width of the laser line with a preset first laser line model and a preset second laser line model to obtain a first similarity and a second similarity, wherein the first similarity represents the similarity between the laser line and the first laser line model, and the second similarity represents the similarity between the laser line and the second laser line model;
determining that the first similarity is greater than the second similarity, and judging that wood exists on the conveyor belt; alternatively, the first and second electrodes may be,
determining that the laser line width of the laser line exceeds the scattering width value of the laser line in the second laser line model and reaches a first set threshold value, and judging that wood exists on the conveyor belt; alternatively, the first and second electrodes may be,
determining that the height position of the laser line exceeds the height position of the laser line in the second laser line model and reaches a second set threshold value, and judging that wood exists on the conveyor belt;
wherein the first laser line model is used for representing the height position and the scattering width value of the laser line acting on the surface of the wood on the conveyor belt, and the second laser line model is used for representing the height position and the scattering width value of the laser line acting on the surface of the conveyor belt.
3. The method of claim 2, further comprising:
acquiring a first laser line image of the surface of the wood on the conveyor belt under the action of a laser line generated by the laser equipment, and constructing a first laser line model based on the first laser line image;
and acquiring a second laser line image of the surface of the conveyor belt under the action of the laser line generated by the laser equipment, and constructing a second laser line model based on the second laser line image.
4. The method of claim 2, wherein identifying the surface defects of the wood based on the laser line height position and the laser line width of the laser line comprises:
constructing a relative height image of the wood based on a difference between a laser line height position of the laser line and a height position in the first laser line model;
constructing a relative scattering image of the wood based on a difference value between the laser line width of the laser line and the scattering width value in the first laser line model;
determining whether the wood has a first defect based on the relative height image;
determining whether a second defect exists in the wood based on the relative scatter image;
wherein the first defect comprises at least one of: edge deletion and moth eye; the second defect includes at least one of: a dead knot and a movable joint.
5. The method of claim 3,
the constructing the first laser line model based on the first laser line image comprises:
acquiring a plurality of frames of the first laser line images;
determining the height position of the laser line on the wood based on the plurality of frames of the first laser line images;
determining a scattering width value of a laser line on the wood based on the plurality of frames of the first laser line images;
the constructing the second laser line model based on the second laser line image comprises:
acquiring a plurality of frames of the second laser line images;
determining the height position of the laser line on the conveyor belt based on the plurality of frames of the second laser line images;
and determining the scattering width value of the laser line on the conveyor belt based on the plurality of frames of the second laser line images.
6. The method of claim 4, wherein constructing the relative height image of the wood based on the difference of the laser line height position of the laser line and the height position in the first laser line model comprises:
and constructing a relative height image on the corresponding area of the wood aiming at the acquired multiple frames of laser line images, wherein the value of each pixel point in the relative height image is determined based on the difference between the height position of the laser line and the height position in the first laser line model.
7. The method of claim 4, wherein constructing a relative scatter image of the wood based on a difference of a laser line width of the laser line and a scatter width value in the first laser line model comprises:
and constructing a relative scattering image on the corresponding area of the wood aiming at the acquired multiple frames of laser line images, wherein the value of each pixel point in the relative scattering image is determined based on the difference value between the laser line width and the scattering width value in the first laser line model.
8. The method of claim 4, further comprising:
if the first defect exists, determining a defect position corresponding to the first defect based on the relative height image; and/or the presence of a gas in the gas,
and if the second defect exists, determining the defect position corresponding to the second defect based on the relative scattering image.
9. A wood surface defect detecting device, comprising:
the device comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring a laser line image of a conveyor belt under the action of a laser line generated by laser equipment, and the laser line extends along the width direction of the conveyor belt on a horizontal plane;
the characteristic extraction module is used for extracting the laser line height position and the laser line width of the laser line based on the acquired laser line image;
the first identification module is used for identifying whether wood exists on the conveyor belt or not based on the laser line height position and/or the laser line width of the laser line and a preset laser line model;
and the second identification module is used for identifying the surface defects of the wood based on the laser line height position and the laser line width of the laser line if the wood exists on the conveyor belt.
10. A wood surface defect detecting apparatus, comprising: a processor and a memory for storing a computer program capable of running on the processor, wherein,
the processor, when executing the computer program, is adapted to perform the steps of the method of any of claims 1 to 8.
11. A wood surface defect detection system, comprising:
the conveying belt is used for conveying the wood on line;
a laser device for generating a laser line acting on the wood;
the image acquisition equipment is used for acquiring a laser line image;
the wood surface defect detecting device of claim 10, being connected to the image capturing device to receive the laser line image captured by the image capturing device.
12. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of the method of any one of claims 1 to 8.
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