WO2019114380A1 - Procédé d'identification de planche de bois, procédé d'apprentissage machine et dispositif d'identification de planche de bois et dispositif électronique - Google Patents

Procédé d'identification de planche de bois, procédé d'apprentissage machine et dispositif d'identification de planche de bois et dispositif électronique Download PDF

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Publication number
WO2019114380A1
WO2019114380A1 PCT/CN2018/109106 CN2018109106W WO2019114380A1 WO 2019114380 A1 WO2019114380 A1 WO 2019114380A1 CN 2018109106 W CN2018109106 W CN 2018109106W WO 2019114380 A1 WO2019114380 A1 WO 2019114380A1
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Prior art keywords
board
dimensional images
sets
image
wood board
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PCT/CN2018/109106
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English (en)
Chinese (zh)
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丁磊
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北京木业邦科技有限公司
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Publication of WO2019114380A1 publication Critical patent/WO2019114380A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns

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  • the present disclosure relates to the field of wood automated processing technologies, and in particular, to a machine learning method, apparatus, electronic device, and computer readable storage medium for wood board identification and board recognition.
  • board sorting is an important part. Whether it is a semi-finished product or a finished product after molding, coloring, drying, etc., it is necessary to classify according to different wood characteristics combined with quality standards.
  • the sorting of the board is done manually. Trained workers, through observation, judge the color, texture, defects, etc. of each piece of wood, and combine the experience to classify a piece of wood into different categories. The wood boards in each category have closer characteristics, achieving a higher product appearance and consistency of quality.
  • the current standard for wood board classification is usually customized by the manufacturer, that is, the current board classification is essentially non-standardized; the machine learning method in the related art adopts a pre-training method, and only a specific model can be trained for a specific manufacturer. Obviously, it cannot be used in multiple vendors.
  • the wood is produced in batches. Each batch of products is highly correlated with the original wood quality and painting process of the batch. There may be huge differences between batches; however, the training model in the related art is completely Depending on the sample lot, dynamic tuning is not possible for different batches. Finally, the change of natural light will have a greater impact on visual recognition.
  • the related technology does not consider the standardization under ambient light changes and cannot adapt to the detection of different environments.
  • Embodiments of the present disclosure provide a machine learning method, apparatus, and computer readable storage medium for wood board recognition.
  • a machine learning method for wood board recognition is provided in an embodiment of the present disclosure.
  • each set of one-dimensional images includes a plurality of one-dimensional images corresponding to different positions of the wooden board, and the plurality of one-dimensional images in each set of one-dimensional images correspond to The same predetermined speed;
  • the board identification type, the plurality of different predetermined speeds, and the plurality of two-dimensional images are used as the plurality of sets of training data to respectively train the board recognition model; each group of the training data includes the category of the board, and the plurality of two-dimensional images.
  • acquiring a plurality of sets of one-dimensional images of the wood board at a plurality of different predetermined speeds including:
  • the board has a relative speed with the linear camera
  • the combination of the relative speed and the sampling frame rate of the linear camera corresponds to a plurality of different predetermined speeds
  • a plurality of sets of one-dimensional images of the board are acquired.
  • acquiring a plurality of sets of one-dimensional images of the wood board at a plurality of different predetermined speeds including:
  • the wood board identification model is trained by using the category of the board, a plurality of different predetermined speeds, and a plurality of two-dimensional images as the plurality of sets of training data, including:
  • the board identification type, a plurality of different predetermined speeds, different illumination conditions, and a plurality of two-dimensional images are used as a plurality of sets of training data to respectively train the wood board recognition model; each group of training data includes a category of wood boards, and multiple A two-dimensional image in a two-dimensional image and its corresponding predetermined velocity and illumination conditions.
  • each of the acquired one-dimensional images in the plurality of sets of one-dimensional images is separately spliced to obtain a plurality of two-dimensional images at a plurality of different predetermined speeds, including:
  • the one-dimensional images of at least two groups are separately spliced to form a plurality of two-dimensional images at different predetermined speeds.
  • the lighting condition includes one or more of a strength of the light source outside the board, a direction of illumination of the external source light, a shooting angle of the image acquiring unit that acquires the one-dimensional image, and an aperture size of the image acquiring unit; the predetermined speed is The relative movement speed between the image acquisition unit and the board.
  • the method further includes:
  • a plurality of one-dimensional images of white reference objects are also acquired while acquiring a plurality of sets of one-dimensional images of the wood board.
  • the board type, the plurality of different predetermined speeds, and the plurality of two-dimensional images are used as the plurality of sets of training data to respectively train the board recognition model, including:
  • a plurality of two-dimensional images are respectively labeled to obtain boundary information of the wooden board.
  • the board type, the plurality of different predetermined speeds, and the plurality of two-dimensional images are used as the plurality of sets of training data to respectively train the board recognition model, and further includes:
  • the board boundary recognition model is trained according to the boundary information of the plurality of two-dimensional images and the wooden board, and the recognition result of the board boundary recognition model includes the boundary information of the board.
  • an embodiment of the present disclosure provides a board identification method, including:
  • the recognition is based on the two-dimensional image and the trained wood board recognition model, and the type of the board and the moving speed are obtained.
  • the obtained one-dimensional images are spliced to obtain a two-dimensional image to be identified, including:
  • At least two sets of one-dimensional images are separately spliced to form at least two two-dimensional images to be identified.
  • identifying according to the two-dimensional image and the trained wood board recognition model, obtaining the category of the board and the moving speed including:
  • the group with the highest confidence is selected from the two sets of confidence estimates as the final recognition result.
  • the method further includes:
  • the kick timing of the board is obtained according to the moving speed of the board.
  • the two-dimensional image and the trained wood board recognition model are identified to obtain the category of the board and the moving speed, including:
  • the boundary information, and the wood board recognition model, the category of the board and the moving speed are obtained.
  • identifying according to the two-dimensional image and the trained wood board recognition model, obtaining the category of the board and the moving speed including:
  • a plurality of two-dimensional images obtained under different illumination conditions are respectively input to the wood board recognition model to obtain a plurality of sets of confidence estimates of the category of the board and the moving speed;
  • the group with the highest confidence is selected from the multi-group confidence estimates as the final recognition result.
  • the method further includes:
  • a plurality of one-dimensional images of white reference objects are also acquired while acquiring a plurality of one-dimensional images of the wood board.
  • a machine learning apparatus for wood board identification comprising:
  • a first obtaining module configured to acquire a plurality of sets of one-dimensional images of the wooden board at a plurality of different predetermined speeds; wherein each set of one-dimensional images includes a plurality of one-dimensional images at different positions of the corresponding wooden board, and each set of one-dimensional images Multiple one-dimensional images in the image correspond to the same predetermined speed;
  • the first splicing module is configured to splicing each set of one-dimensional images in the acquired plurality of one-dimensional images to obtain a plurality of two-dimensional images at a plurality of different predetermined speeds;
  • the training module is configured to train the wood board recognition model as a plurality of sets of training data by using a category of the board, a plurality of different predetermined speeds, and a plurality of two-dimensional images; each group of the training data includes a category of the board, a two-dimensional image of the plurality of two-dimensional images and a corresponding predetermined speed; the recognition result of the wood board recognition model includes a category of the wood board and a moving speed.
  • the first obtaining module includes:
  • the first acquisition sub-module is configured to acquire a plurality of sets of one-dimensional images of the wood board in a case where the board has a relative speed with the linear camera and the combination of the relative speed and the sampling frame rate of the linear camera corresponds to a plurality of different predetermined speeds.
  • the first obtaining module includes:
  • a second obtaining sub-module configured to acquire a plurality of sets of one-dimensional images of the wood board at a plurality of different predetermined speeds under different lighting conditions
  • Training modules including:
  • the first training sub-module is configured to train the wood board recognition model as a plurality of sets of training data by using a category of the board, a plurality of different predetermined speeds, different illumination conditions, and a plurality of two-dimensional images; each group of the plurality of sets of training data
  • the training data includes the category of the board, a two-dimensional image of the plurality of two-dimensional images, and their corresponding predetermined speeds and lighting conditions.
  • the first splicing module includes:
  • a first grouping sub-module configured to divide each set of one-dimensional images in the acquired plurality of sets of one-dimensional images into at least two groups according to an image acquisition time sequence and/or an image order;
  • the first splicing sub-module is configured to splicing the one-dimensional images of the at least two groups separately to form a plurality of two-dimensional images at different predetermined speeds.
  • the lighting condition includes one or more of a strength of the light source outside the board, a direction of illumination of the external source light, a shooting angle of the image acquiring unit that acquires the one-dimensional image, and an aperture size of the image acquiring unit; the predetermined speed is The relative movement speed between the image acquisition unit and the board.
  • the device further includes:
  • the second obtaining module is configured to acquire a plurality of one-dimensional images of the white reference object while acquiring the plurality of sets of one-dimensional images of the wooden board.
  • the first splicing module includes:
  • a second splicing sub-module configured to splicing each set of one-dimensional images in the plurality of sets of one-dimensional images to obtain a plurality of two-dimensional images
  • the labeling sub-module is configured to label a plurality of two-dimensional images separately to obtain boundary information of the board.
  • the training module further includes:
  • the second training sub-module is configured to train the board boundary recognition model according to the boundary information of the plurality of two-dimensional images and the wooden board, and the recognition result of the board boundary recognition model includes the boundary information of the board.
  • the functions can be implemented in hardware or in hardware by executing the corresponding software.
  • the hardware or software includes one or more modules corresponding to the functions described above.
  • the structure of the board-recognized machine learning device includes a memory and a processor for storing one or more machine learning devices that support wood board recognition to perform the machine learning method of board identification in the first aspect above.
  • Computer instructions, the processor being configured to execute computer instructions stored in the memory.
  • the board-aware machine learning device may also include a communication interface with which the machine learning device for wood board identification communicates with other devices or communication networks.
  • an embodiment of the present disclosure provides a board identifying device, including:
  • a third obtaining module configured to acquire a plurality of one-dimensional images of the wood board
  • a second splicing module configured to splicing the acquired one-dimensional images to obtain a two-dimensional image to be recognized
  • the identification module, the splicing module identifies the two-dimensional image and the trained wood board recognition model, and obtains the category of the board and the moving speed.
  • the second splicing module includes:
  • a second grouping sub-module configured to divide the acquired plurality of one-dimensional images into at least two sets of one-dimensional images according to an image acquisition time sequence and/or an image order;
  • the third splicing sub-module is configured to splicing at least two sets of one-dimensional images to form at least two two-dimensional images to be identified.
  • the identification module includes:
  • a first identification sub-module configured to input at least two two-dimensional images into the wood board recognition model respectively, to obtain two types of confidence estimates of the category of the board and the moving speed;
  • the first selection sub-module is configured to select the group with the highest confidence from the two sets of confidence estimates as the final recognition result.
  • the method further includes:
  • the kick leg module is configured to obtain the kick timing of the board based on the moving speed of the board.
  • the identification module includes:
  • a second identification sub-module configured to obtain boundary information of the wooden board according to the two-dimensional image and the trained boundary recognition model
  • the third identification sub-module is configured to obtain the category of the wooden board and the moving speed according to the two-dimensional image, the boundary information, and the wood board recognition model.
  • the identification module includes:
  • a fourth identification sub-module configured to input a plurality of two-dimensional images obtained under a plurality of different illumination conditions into the wood board recognition model respectively, to obtain a plurality of sets of confidence estimates of the category of the board and the moving speed;
  • the second selection sub-module is configured to select the group with the highest confidence from the plurality of sets of confidence estimates as the final recognition result.
  • the method further includes:
  • the fourth obtaining module is configured to acquire a plurality of one-dimensional images of the white reference object while acquiring the plurality of one-dimensional images of the wood board.
  • the functions can be implemented in hardware or in hardware by executing the corresponding software.
  • the hardware or software includes one or more modules corresponding to the functions described above.
  • the structure of the board identifying device includes a memory and a processor for storing one or more computer instructions for supporting the board identifying device to perform the board identifying method in the first aspect, the processor being configured to use Computer instructions stored in the execution memory.
  • the board identification device may also include a communication interface for the board identification device to communicate with other devices or communication networks.
  • an embodiment of the present disclosure provides an electronic device, including a memory and a processor; wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the first Aspect or method step of the second aspect.
  • an embodiment of the present disclosure provides a computer readable storage medium for storing computer instructions for a wood board identification machine learning device or a wood board identification device, including a machine for performing wood board recognition in the above first aspect
  • the computer method involved in the learning method or the second aspect of the board identification method is a computer readable storage medium for storing computer instructions for a wood board identification machine learning device or a wood board identification device, including a machine for performing wood board recognition in the above first aspect.
  • a plurality of sets of one-dimensional images of wood boards at different speeds are acquired by a linear camera, and a plurality of sets of one-dimensional images are further spliced to form a plurality of two-dimensional images corresponding to different speeds, thereby utilizing speed, real categories of wood boards, and corresponding
  • the two-dimensional image respectively trains the wood board recognition model, so that the wood board recognition model after the training can automatically recognize the type and speed of the board.
  • the embodiment of the present disclosure collects a one-dimensional image of a wooden board at different speeds, and uses the same wooden board image samples at different speeds, the type of the wooden board, and the speed to train the machine learning model, so that the wood board recognition model obtained by the training can be accurately
  • the image of the wood board collected at different speeds is recognized, and the relative movement speed of the board is also recognized.
  • the wood board recognition model obtained by the present disclosure can accurately classify the board regardless of the environment and any speed, and perform the sorting operation at the exact kick time.
  • a separate device such as a photoelectric sensor, to obtain the time of arrival of the board, but the classification time can be determined by classification learning.
  • FIG. 1 shows a flow chart of a machine learning method for wood board recognition in accordance with an embodiment of the present disclosure
  • FIG. 2 A schematic diagram of a convolutional neural network in accordance with an embodiment of the present disclosure is shown in FIG. 2;
  • FIG. 3 is a schematic view showing the overall structure of a wood sorting system according to an embodiment of the present disclosure
  • FIG. 4 shows a flow chart of step S102 according to the embodiment shown in Figure 1;
  • FIG. 5 shows a flow chart of step S103 according to the embodiment shown in Figure 1;
  • FIG. 6 illustrates an example of performing boundary labeling on an image sample according to an embodiment of the present disclosure
  • FIG. 7 illustrates a flowchart of a wood board identification method according to an embodiment of the present disclosure
  • FIG 8 shows a flow chart of step S702 according to the embodiment shown in Figure 7;
  • FIG. 9 is a block diagram showing the structure of a machine learning device for wood board recognition according to an embodiment of the present disclosure.
  • FIG. 10 is a block diagram showing the structure of a wood board identifying apparatus according to an embodiment of the present disclosure.
  • FIG. 11 is a block diagram showing the structure of an electronic device suitable for implementing a machine learning method for wood board recognition according to an embodiment of the present disclosure.
  • the camera In prior art machine learning applications, the camera is required to use low precision cameras as much as possible to reduce costs. This is because some classification algorithms do not require very high precision image data to achieve accurate classification.
  • the machine learning method has higher requirements on the resolution of the camera.
  • Conventional cameras use rectangular sensors, which have the advantage of being able to obtain two-dimensional image data that is easy to process. However, this also limits the resolution of the sample. Linear cameras have higher one-dimensional resolution and variable sampling periods, so they have better application potential in wood processing.
  • FIG. 1 illustrates a flow chart of a machine learning method for wood board recognition in accordance with an embodiment of the present disclosure.
  • the machine learning method for wood board recognition includes the following steps S101-S103:
  • step S101 a plurality of sets of one-dimensional images of the wooden board at a plurality of different predetermined speeds are acquired; wherein each set of one-dimensional images includes a plurality of one-dimensional images at different positions of the corresponding wooden boards, and each of the one-dimensional images in each group One-dimensional images correspond to the same predetermined speed;
  • step S102 each set of one-dimensional images in the acquired plurality of sets of one-dimensional images are separately spliced to obtain a plurality of two-dimensional images at a plurality of different predetermined speeds;
  • step S103 the board type, the plurality of different predetermined speeds, and the plurality of two-dimensional images are used as the plurality of sets of training data to respectively train the board recognition model; each group of the training data includes the category of the board, and more A two-dimensional image of the two-dimensional image and the corresponding predetermined speed; the recognition result of the wood-board recognition model includes the category of the wood board and the moving speed.
  • the collected wood board image is a high-precision one-dimensional image, and images of different moving speeds are collected for the same wood board sample, so that the training sample is more abundant, and Can adapt to the recognition of images acquired under various conditions.
  • the different moving speeds here refer to the relative speed between the board and the image acquisition device, ie the linear camera.
  • the one-dimensional image can be acquired by a linear camera. Since the linear camera can only acquire one one-dimensional image at each time point, the one-dimensional image cannot be directly used for subsequent learning or classification. Therefore, a plurality of one-dimensional data can be spliced into one two-dimensional image, and the two-dimensional image can contain image information of a part or the whole board.
  • the predetermined speed may be a relative speed between a predefined board and a linear camera.
  • a predetermined speed When collecting a one-dimensional image of a wooden board, when the relative speed between the wooden board and the linear camera is a predetermined speed, a one-dimensional image of the wooden board is collected, and one one-dimensional image is collected at each time point in sequence, and more is collected at the same predetermined speed.
  • One-dimensional images are a set of one-dimensional images. After collecting a plurality of one-dimensional images of part or the whole board, the relative speed between the board and the linear camera can be changed, and the acquisition is performed again, and finally a plurality of sets of one-dimensional images at different predetermined speeds are obtained. By splicing a set of one-dimensional images acquired at the same predetermined speed, a two-dimensional image corresponding to the predetermined speed is obtained, and finally a plurality of two-dimensional images corresponding to different predetermined speeds can be obtained.
  • the category of the board, the predetermined speed, and the two-dimensional image corresponding to the predetermined speed are used as a set of training data, and the same board corresponds to multiple sets of training data, and each group of training data
  • the two-dimensional image is used as an input to the machine learning model, and the corresponding predetermined speed and board type are used as outputs to train various parameters of the machine learning model.
  • the result of the machine learning model is converged, and finally the trained wood board recognition model is obtained.
  • the machine learning model may include, but is not limited to, one or a combination of a convolutional neural network, a feedback neural network, a deep learning network, a decision forest, a Bayesian network, a support vector machine.
  • the neural network is taken as an example to introduce the principle and process of model training in detail.
  • the neural network may be an automatic classification model, a regression model or a decision model, and the neural network may be one or a combination of a convolutional neural network and a deep neural network.
  • the neural network may comprise a neural network comprising a plurality of layers, each layer comprising a plurality of nodes, and a trainable weight (i.e., parameters of the aforementioned machine learning model) between the two adjacent nodes.
  • FIG. 2 A schematic of a convolutional neural network is shown in Figure 2, which includes multiple convolutional and downsampled layers and a fully connected layer.
  • the convolutional layer is the core module of the convolutional neural network. By convolving with a filter, multiple nodes of the previous layer are connected to the nodes of the next layer. In general, each node of the convolutional layer is only connected to a part of the nodes of the previous layer.
  • the downsampling layer can use a max-pooling method to reduce a set of nodes into one node, using a method of nonlinearity taking the maximum value.
  • a fully connected layer After passing through multiple convolutional and downsampled layers, a fully connected layer is ultimately used to generate the output of the classification.
  • the fully connected layer connects all nodes of the previous layer to all nodes of the latter layer, which is related to a traditional neural
  • the network is similar.
  • a training algorithm such as a gradient descent algorithm
  • a gradient descent algorithm can be used to change the filter weight values in the neural network, thereby minimizing the difference in classification between the output and the sample data.
  • the training process can be done locally or in the cloud.
  • the determined classification of the board and the relationship between the 2D image and the custom classification, as well as the 2D image, etc. can be uploaded to the cloud.
  • the cloud server uses the obtained custom classification, the relationship between the two-dimensional image and the custom classification, and the two-dimensional image to train the neural network, and deploys the trained wood board recognition model to the local.
  • the category of the board can be predetermined. First determine the board samples, then customize the board samples, for example, classify the 1-3 board samples into Class A categories, class 4-8 boards into Class B categories, and Class 9-10 boards into Class C categories. . Because it is a custom classification, it can be customized according to the specific conditions of the board factory and the actual classification. For example, the boards of No. 1, 3, and 5 are classified into Class A categories, and the remaining board samples are classified into Class B categories. In the factory, custom classification is performed according to the actual situation of the factory. This kind of custom classification is more suitable for the actual situation of different board factories and the classification requirements, and the classification is more flexible and convenient. The specific implementation of the classification is done manually by experience. How many categories are set and which sample belongs to which category is also implemented manually. Manual sorting can be done based on different features of the board, such as color, texture, defects, and the like.
  • the step S103 is to obtain a plurality of sets of one-dimensional images of the board at a plurality of different predetermined speeds, and further includes:
  • the board has a relative speed with the linear camera
  • the combination of the relative speed and the sampling frame rate of the linear camera corresponds to a plurality of different predetermined speeds
  • a plurality of sets of one-dimensional images of the board are acquired.
  • a plurality of methods can be employed.
  • One way is to move the board to the linear camera with the linear camera fixed.
  • the movement of the board is achieved by a conveyor belt, and a linear camera is mounted above the conveyor belt to capture images.
  • 3 is a block diagram showing the structure of a wood board sorting system for sampling wooden board images in an embodiment of the present disclosure. The structural details of Figure 3 will be described in detail in the section on Woodboard Classification and Identification.
  • the wood board sample enters the finishing area B1, in which the wood board samples are finished, guided, and then sequentially entered into the determination area B2 (image sampling area).
  • one or more linear cameras on the pipeline capture the one-dimensional image of the wood sample at a predetermined speed with a very rapid speed.
  • the conveyor moves at a different predetermined speed each time: V1 represents a predetermined speed of 0, V2.
  • V3....Vn represents the speed before the predetermined speed is from 0 to the line belt of the B2 area, and V(n+1) represents the B2 line speed in order to obtain a one-dimensional image of the board at different predetermined speeds.
  • the linear camera moves toward the plank sample with the plank sample fixed.
  • one or more linear cameras scan the wood sample at a predetermined speed and capture the image of the wood sample at a very fast rate: V1 represents speed 0, V2, V3....Vn represents 0 to B2, respectively.
  • V1 represents speed 0, V2, V3....Vn represents 0 to B2, respectively.
  • V(n+1) represents the B2 pipeline speed, in order to obtain a one-dimensional image of the plank samples at different speeds. This method is suitable for cases where the wooden board sample is large and inconvenient to move.
  • the moving speed can also be simulated by adjusting the frame rate of the linear camera.
  • the first frame rate is used to collect the one-dimensional image in the first half of the board sample
  • the second frame rate is used to collect the one-dimensional image in the second half of the board sample, so that two different differences of the same board can be obtained.
  • An image sample of a predetermined speed For another example, the board is fixed, and when the linear camera is moved, the image samples at various predetermined speeds can also be obtained by adjusting the frame rate of the linear camera.
  • a linear camera moves the board several times at the same speed V, but scans the sample using different frame rates, for example, f0, f2, f3....fn, and the obtained board image and the board obtained at different moving speeds.
  • the image is the same.
  • both the wood sample and the linear camera may be moved, and the moving direction may be relative or the same direction, as long as the relative motion speed between the two is not zero (ie, At the same time, the linear camera uses a certain frame rate to scan the wood sample, and the aforementioned predetermined speed is formed by the combination of the relative motion speed and the sampling frame rate.
  • the moving speed of the wood board sample, the moving speed of the linear camera, the sampling frame rate, and the predetermined speed are all variable.
  • the relative motion speed of the board is v1+v2
  • the one-dimensional image obtained by the linear camera using the standard frame rate scan is predetermined.
  • a one-dimensional image obtained by scanning at a standard frame rate of 2 times is a one-dimensional image at a predetermined speed of (v1+v2)/2, and is scanned using a 1/2 standard frame rate.
  • the one-dimensional image is a one-dimensional image at a predetermined speed of 2 (v1+v2). That is, in the embodiment of the present application, the predetermined speed can be obtained by adjusting one or more of the moving speed of the wood board sample, the moving speed of the linear camera, and the sampling frame rate.
  • the step S101 is to obtain a plurality of sets of one-dimensional images of the board at a plurality of different predetermined speeds, including:
  • the wood board identification model is trained by using the category of the board, a plurality of different predetermined speeds, and a plurality of two-dimensional images as the plurality of sets of training data, including:
  • the board identification type, a plurality of different predetermined speeds, different illumination conditions, and a plurality of two-dimensional images are used as a plurality of sets of training data to respectively train the wood board recognition model; each group of training data includes a category of wood boards, and multiple A two-dimensional image in a two-dimensional image and its corresponding predetermined velocity and illumination conditions.
  • image acquisition under different illumination conditions is achieved by transforming the aperture of the linear camera during image acquisition; it is also possible to set the light source around the linear camera to achieve different brightness by adjusting the brightness of the light source or the illumination direction of the light source.
  • the lighting conditions For example, as shown in FIG. 3, one or more light sources are added to the B2 area production line, for example, the light source may be a flat type LED lamp: L1, L2 togetherLn, the LED lamp can provide relatively uniform illumination, and The brightness of the light source can be sequentially increased or decreased by the control method to obtain samples of the product under different light conditions; the aperture size and the brightness or illumination direction of the light source can be simultaneously changed to achieve different illumination conditions.
  • the illumination condition of the LED lamp is used to raise the basic brightness of the image acquisition to a satisfactory level, and at the same time, by changing the size of the aperture to obtain the illumination level of the upper and lower floating of the basic brightness.
  • the combination of the two enables multiple illumination conditions to be achieved within a satisfactory range. Since the linear camera only collects one-dimensional images in a unit time, the entire board is multi-collected and finally spliced into a two-dimensional image. Therefore, different illumination intensities can be used at different times by a method synchronized with the illumination device.
  • s1 illumination intensity is used at t1 sampling time; at t2 sampling time, s2 illumination intensity is used; t3 sampling time is used, s3 illumination intensity is used, and so on, and multiple one-dimensional images are obtained.
  • the images of the odd moments are assembled into a first image sample, the even time instants are assembled into a second image sample, and after the wood board samples are scanned, two image samples under two different illumination conditions are obtained.
  • the s1 light intensity can be used during the first half of the scan area of the board sample, and the s2 light intensity can be used during the second half of the scan area of the board sample, or image samples at two light intensities can be obtained.
  • This method of changing the illumination intensity can be used in conjunction with the method of changing the aperture to obtain image samples with more illumination intensity.
  • each set of one-dimensional images in the acquired plurality of one-dimensional images is separately spliced to obtain a plurality of different predetermined speeds.
  • the step of multiple two-dimensional images further includes the following steps:
  • step S401 each set of one-dimensional images in the acquired plurality of sets of one-dimensional images are respectively divided into at least two groups according to an image acquisition time sequence and/or an image order;
  • step S402 at least two groups of one-dimensional images are separately spliced to form a plurality of two-dimensional images at different predetermined speeds.
  • multiple one-dimensional images in the same group can be divided into at least two groups by chronological order of image acquisition; for example, the first half of the same group of one-dimensional images are divided into one group, and the second half is one-dimensional.
  • the images are divided into a group; the plurality of one-dimensional images in the same group can also be divided into at least two groups by image order; for example, one-dimensional images in the same group with an odd order are divided into one group, and the order is evenly divided into one group.
  • the same group of one-dimensional images can be divided into multiple groups according to other methods, which can be set according to actual conditions, and details are not described herein again.
  • the first and second parts of the obtained one-dimensional image are divided into two parts according to the chronological order, and finally the one-dimensional images of the front and the back are respectively spliced to obtain two one-dimensional images.
  • 2N two-dimensional images will be obtained, and the number of groups in N and multiple sets of one-dimensional images is the same.
  • different illumination conditions can be used, so that the two two-dimensional images obtained have the same predetermined speed, but the illumination conditions are different.
  • the acquired set of one-dimensional images may be divided into two parts according to the image order, that is, the one-dimensional images of the odd-numbered and even-numbered ones in the set of one-dimensional images acquired at the same predetermined speed are respectively divided into two groups.
  • the one-dimensional images included in each group are spliced into a two-dimensional image, and two two-dimensional images can be obtained at the same predetermined speed. At different speeds, a 2N two-dimensional image can be obtained.
  • different illumination conditions can be used, so that the two two-dimensional images obtained have the same predetermined speed, but the illumination conditions are different.
  • the lighting condition includes one or more of a strength of the light source outside the board, a direction of illumination of the external source light, a shooting angle of the image acquiring unit that acquires the one-dimensional image, and an aperture size of the image acquiring unit; the predetermined speed is The relative movement speed between the image acquisition unit and the board.
  • the method further includes:
  • a plurality of one-dimensional images of white reference objects are also acquired while acquiring a plurality of sets of one-dimensional images of the wood board.
  • a reference image can be set during image acquisition.
  • a white reference object is provided to ensure that the image of the wood sample is captured simultaneously with the image of the white reference object.
  • a white reference object can be used to provide a reference for white balance, brightness, or other image parameters.
  • each set of training data includes at least one board category (ie, a customized product category) and a speed, and may also include a lighting condition and/or A camera angle label.
  • board category ie, a customized product category
  • speed may also include a lighting condition and/or A camera angle label.
  • a Sample can be a combination of multiple one-dimensional image data.
  • the speed information can be determined by the true relative movement speed and the frame rate.
  • Multiple images Sample can be obtained from the same board.
  • step S103 is to train the wood board recognition model as a plurality of sets of training data, that is, a category of the board, a plurality of different predetermined speeds, and a plurality of two-dimensional images.
  • the steps further include:
  • step S501 each set of one-dimensional images in the plurality of sets of one-dimensional images are separately spliced to obtain a plurality of two-dimensional images;
  • step S502 a plurality of two-dimensional images are respectively labeled to obtain boundary information of the board.
  • each image sample is a two-dimensional image sample, and the image sample itself contains a boundary information, that is, how the one-dimensional image data obtained by the linear camera is uninterrupted is cut and spliced into an independent A sample of a two-dimensional image.
  • An annotation uses the image itself as a boundary, that is, without any additional annotations, using only one independent two-dimensional image that contains both a woodboard image and a useless background image as a sample.
  • Another type of annotation uses an additional boundary label to independently mark the boundaries of the board image to distinguish the board image from the background image.
  • Figure 6 shows an example of boundary labeling of image samples, where the start and end boundaries are used to determine a separate board image and the side borders are used to determine the boundaries between the board image and the background image.
  • the step S103 that is, the step of training the board recognition model by using the category of the board, the plurality of different predetermined speeds, and the plurality of two-dimensional images as the plurality of sets of training data, further includes:
  • the board boundary recognition model is trained according to the boundary information of the plurality of two-dimensional images and the wooden board, and the recognition result of the board boundary recognition model includes the boundary information of the board.
  • the linear camera can continuously acquire one-dimensional images of a plurality of wood boards moving or arranged in a pipeline, and splicing to obtain a continuous two-dimensional image.
  • a cutting model that is, a board boundary recognition model
  • a board boundary recognition model can be trained to cut a continuous two-dimensional image, that is, to identify boundary information in the two-dimensional image, and to divide the boundary information into a plurality of two-dimensional images. Get a 2D image sample each containing only one board image.
  • the cutting model can use a separate neural network or share a neural network with the wood board recognition model. In order to facilitate the distinction between the board and the board identification network, the function of the network is described only in terms of functionality. In actual implementation, there may not be a separate entity or output value.
  • the cutting model can be trained by the above-mentioned image data marked with a boundary to obtain a decision strategy. After receiving the uninterrupted two-dimensional image, a decision of the starting boundary can be generated at time t1, and an end is generated at time t2. The judgment of the border. Further, the image data at times t1 and t2 is composed into an image containing only one independent wooden board sample by a simple image processing method. By continuously using the network, multiple independent two-dimensional image samples can be generated without interruption, and that each image sample contains only one complete woodboard image data. In addition to this, the neural network can also perform the identification of the side boundaries and reject the irrelevant background image data.
  • image data may also be acquired using a plurality of linear cameras.
  • Multiple linear cameras simultaneously acquire image data of a wooden board sample at different shooting angles and generate multiple sets of training data.
  • the plurality of sets of training data include labels such as a board type, a corresponding speed, and a corresponding shooting angle, and may of course include a label such as a corresponding illumination.
  • Multiple linear cameras can also use a variety of different lighting conditions. For example, the scanning area is divided into two areas, A and B, which are opaque, and linear cameras a and b are installed respectively, and s1 and s2 are used. After the board sample passes through the scanning area, two Samples under different lighting conditions are obtained. In the same way, samples under multiple parameters such as shooting angle and moving speed can also be obtained.
  • FIG. 7 illustrates a flow chart of a wood board identification method in accordance with an embodiment of the present disclosure. As shown in FIG. 7, the wood board identification method includes the following steps S701-S703:
  • step S701 acquiring a plurality of one-dimensional images of the wood board
  • step S702 the acquired plurality of one-dimensional images are spliced to obtain a two-dimensional image to be identified;
  • step S703 recognition is performed based on the two-dimensional image and the trained wood board recognition model, and the type of the board and the moving speed are obtained.
  • the one-dimensional image can be acquired by a linear camera. Since the linear camera can only acquire one one-dimensional image at each time point, the one-dimensional image cannot be directly used for subsequent recognition. Therefore, a plurality of one-dimensional data can be spliced into one two-dimensional image, and the two-dimensional image can contain image information of a part or the whole board.
  • the wood board recognition model may be pre-trained, such as a wood board recognition model obtained using the machine learning method shown in FIG. Since the wood board recognition model is trained through two-dimensional images, speeds, and categories, the type of the board, the moving speed, and the like can be identified by the image samples of the board to be identified.
  • the moving speed of the board is the relative moving speed, that is, the relative moving speed between the linear camera and the board that captures the image.
  • the wood board identification method can be performed in the control device of the wood board sorting system shown in FIG.
  • the board sorting system includes: a conveying device 301, a linear image collecting device 302, a control device 303, and a sorting device 304;
  • the wood to be classified is placed on the conveying device 301, and is conveyed backward by the conveying device 301;
  • the linear image capturing device 302 is disposed in alignment with the transmitting device 301 for collecting a one-dimensional linear image of the wood to be classified, and the output end of the linear image capturing device 302 is coupled to the control device;
  • the output end of the control device 303 is coupled to the classification device 304.
  • the control device 303 outputs a direction signal and a time signal to the classification device 304 according to the one-dimensional image collected by the linear image acquisition device.
  • the sorting means 304 is disposed above the end of the transport means 301, and moves the board to be sorted out of the transport means 301 in the direction indicated by the direction signal at the point in time indicated by the time signal.
  • the transfer device is preferably a conveyor belt including, but not limited to, a belt, gear or chain driven conveyor belt device.
  • the transmitting device 301 can be further divided into an image capturing area 305 and a kicking area 306, wherein the linear image capturing device 302 is disposed in alignment with the image capturing area 305, and after the wood to be classified enters the image capturing area 305, The one-dimensional image of the wood to be classified is collected; the sorting device 304 is disposed in the kicking area 306 for moving the wood to be classified out to the designated position according to the direction signal and the time signal output by the control device 303.
  • the one-dimensional image collected by the linear image capturing device simultaneously performs the classification identification of the wood to be classified and the recognition of the kick machine control.
  • at least one photoelectric sensor can be omitted, and on the other hand, the identification and control are performed.
  • Speed and accuracy are higher than the relevant technology, greatly improving the efficiency of board sorting and reducing costs.
  • the linear image acquisition device collects a one-dimensional image of the wood to be classified in a relatively moving state under a certain natural and/or artificial illumination environment.
  • the board moves relative to the linear acquisition device with a relative speed between the two.
  • the relative movement speeds in the present disclosure are not fixed, but may be variable, and are preferably varied. In the case where the relative moving speed is changed, the wood board sample images at different relative moving speeds can be better obtained.
  • the linear image capture device is fixedly mounted above the transport device, and the linear image capture device is positioned to capture images of the image capture region of the transport device.
  • image acquisition image acquisition at different speeds can be achieved by transforming the sampling frame rate of the linear image capture device; optionally, the wood board sorting system further includes at least one LED light source 307, and at least one LED light source 307 is disposed at the transmitting device 301.
  • the image acquisition area 306 is illuminated; preferably, at least one LED light source 307 is disposed adjacent to or integrated with the linear image acquisition device 302. Different illumination conditions can be realized by adjusting the brightness of the LED light source 307 or the illumination direction of the light source.
  • the LED light source can be a flat type LED light to provide relatively uniform illumination.
  • the brightness of the light source can be set to be sequentially increased or decreased to obtain a product.
  • Samples in different light conditions; different lighting conditions can also be achieved by transforming one or more of aperture size, source brightness, and illumination direction.
  • the illumination of the LED light is used to increase the basic brightness of the image acquisition to a satisfactory level, and at the same time, by varying the aperture size, the illumination level of the base brightness is obtained.
  • the angle of the image acquisition device can also be dynamically adjusted to capture the image of the wood sample at different angles.
  • the combination of various transformations can be further performed in terms of time and/or order, that is, different transformation combinations can be performed in different sequences at different times to acquire images.
  • a reference image may also be set during image acquisition to assist in improving the accuracy of image recognition.
  • a reference object area is provided in the image acquisition area 306 of the transport device 301, and a reference object 308 is disposed in the reference object area; wherein the reference object area and the reference object 308 remain stationary (ie, do not move with the transport device); linear image
  • the collecting device needs to ensure that the image of the wood board sample and the image of the reference object are collected at the same time.
  • the reference object has a white surface and the white reference object can be used to provide a standard reference for white balance, brightness or other image parameters.
  • the linear image capture device may be one or more linear cameras (such as a plurality of linear cameras, etc.), simultaneously acquiring an image of a wooden board sample by one or more linear cameras, and generating a sample data.
  • the sample data includes corresponding labels of illumination, speed, acquisition angle, etc., and the data collected by the plurality of sensors becomes a combination of multiple angle image data with respect to one image sensor.
  • control device 303 is also coupled to one or more computer devices.
  • the detection of the wood board classification may be done locally or in the cloud.
  • the collected image data is sent locally to the cloud, and the information that the cloud can provide includes, but is not limited to, the definition of the wood board classification, the sample image of each category, the classification recognition model, and the classification detection result.
  • step S702 is a step of splicing the acquired one-dimensional images to obtain a two-dimensional image to be recognized, and further includes:
  • step S801 the acquired plurality of one-dimensional images are divided into at least two sets of one-dimensional images according to an image acquisition time sequence and/or an image order;
  • step S802 at least two sets of one-dimensional images are respectively spliced to form at least two two-dimensional images to be identified.
  • the wooden board is sent to the image collection area through the conveyor belt, and the wooden board completes the image acquisition through the scanning area of the linear camera during the movement, and processes the collected one-dimensional images through the linear camera to obtain the image.
  • a two-dimensional image is entered and the acquired two-dimensional image is entered into a trained wood board recognition model.
  • the board is fixed in an area, and the entire board is scanned by moving a linear camera to acquire a plurality of one-dimensional images and obtain a two-dimensional image.
  • an external light source such as an LED light source
  • the light source provides a uniform illumination to enhance the underlying brightness of the image.
  • a linear camera synchronized with an external light source different lighting conditions can be transformed at different times, and image samples under multiple illumination conditions can be obtained.
  • the acquired one-dimensional one-dimensional image can be divided into two parts, that is, divided into two parts according to chronological order, and finally one-dimensional images of the front and back two parts are respectively spliced to obtain two one-dimensional images.
  • different illumination conditions can be used, so that the two two-dimensional images obtained have the same predetermined speed, but the illumination conditions are different.
  • a set of one-dimensional images acquired at the same predetermined speed can be divided into two parts, that is, the one-dimensional images in which the sampling order of the obtained one-dimensional image is divided into odd and even digits are respectively divided into two groups, each group The included one-dimensional images are stitched into a two-dimensional image, and two two-dimensional images can be obtained at the same predetermined speed.
  • different illumination conditions can be used, so that the two two-dimensional images obtained have the same predetermined speed, but the illumination conditions are different.
  • the lighting condition includes one or more of a strength of the light source outside the board, a direction of illumination of the external source light, a shooting angle of the image acquiring unit that acquires the one-dimensional image, and an aperture size of the image acquiring unit; the predetermined speed is The relative movement speed between the image acquisition unit and the board.
  • step S703 the two-dimensional image and the trained wood board recognition model are identified, and the category of the board and the moving speed are obtained, including:
  • the group with the highest confidence is selected from the two sets of confidence estimates as the final recognition result.
  • two two-dimensional images obtained by the wood board to be recognized when two two-dimensional images obtained by the wood board to be recognized are identified, two two-dimensional images to be recognized may be input to the wood board recognition model respectively, and the final recognition result is trusted. A higher-valued group is used as the final recognition result, which can increase the recognition accuracy.
  • the trained wood board recognition model analyzes the input two-dimensional image to be identified to determine the moving speed and category of the board. If, during training, each type of training sample contains training samples under different lighting conditions, the trained wood board recognition model can implement reliable speed and category recognition under any lighting conditions.
  • any lighting condition may be an illumination condition that floats up and down around the base brightness under a basic brightness condition, such as by illumination of an external source of the LED. If the previous sample collection does not contain multiple lighting conditions, but only the category identification, the recognition error may be caused by changes in lighting conditions. This is due to the lack of sample data under multi-light conditions, and the convolutional neural network cannot correct the effects of illumination on image samples.
  • the color feature of the image is inevitably used in the recognition process, and the feature changes with the change of illumination, so that different illumination changes the final classification result.
  • image samples under multiple illumination conditions of the same wood sample are used, multiple sets of confidence estimates will be obtained, preferably with the result with the highest confidence value as the final result.
  • a reference image such as a white reference image
  • the image is captured into an image data simultaneously with the wood board image, which can be used as a reference for white balance and brightness or other image parameters.
  • the white reference described above can correct the image for white balance and brightness. Since the white reference can be considered as an image known, the change in illumination of the woodboard image can be derived from the change in illumination of the white reference. Since the speed of the board on the conveyor belt is not always equal to the speed of the conveyor belt, each new board can enter the image acquisition area at an arbitrary speed. Since the training samples contain samples at different speeds, the board identification model is also Can distinguish the speed of movement of the board.
  • the predetermined kick time may be determined according to the moving speed of the wooden board, and the kicking operation, that is, the sorting operation, is performed according to the determined wooden board type at the predetermined kicking time, and the wooden board is kicked into the corresponding category. .
  • An example neural network output is as follows:
  • the output of the board recognition model may be:
  • Sample 1a can be used for the final classification judgment at this time. This is due to the fact that Sample 1a is more suitable for illumination conditions due to natural light and external light source synthesis, thus making the confidence value higher.
  • the output of the board recognition model is the final estimate of the confidence of each category (eg board type, speed, shooting angle, etc.). Based on these estimates, the category with the highest confidence can be selected as the final output.
  • category eg board type, speed, shooting angle, etc.
  • the category with the highest confidence can be selected as the final output.
  • the above implementation only uses neural networks as a basic method.
  • Other similar machine learning methods such as support vector machine, KNN, RNN, K-means, decision forest, etc., can continue the same method and process. Implement a solution based on other machine learning methods. .
  • the action performed by the kick can be based on a time-to-speed mapping relationship, for example:
  • V is the resulting velocity estimate and a, b are predefined parameters. If the conveyor belt changes, such as when the distance between the kicker and the camera changes, only the values of a and b need to be changed without retraining the entire neural network.
  • the mapping relationship between kick time and speed can also be achieved in many ways, not limited to methods.
  • step S703 the two-dimensional image and the trained wood board recognition model are identified, and the steps of the board type and the moving speed are obtained, including:
  • the boundary information, and the wood board recognition model, the category of the board and the moving speed are obtained.
  • the linear camera may acquire a one-dimensional image uninterruptedly on a plurality of planks that are moved or arranged in a pipeline, and spliced to obtain a continuous two-dimensional image.
  • a trained cutting model that is, a board boundary recognition model
  • the cutting model can generate a decision of the starting boundary at time t1 and generate a decision to end the boundary at time t2.
  • the image data at times t1 and t2 is composed into an image containing only one independent wooden board sample by a simple image processing method.
  • multiple independent two-dimensional image samples can be generated without interruption, and that each image sample contains only one complete woodboard image data.
  • the neural network can also perform the identification of the side boundaries and reject the irrelevant background image data.
  • the cut image data containing the individual boards is used as input, and an output value is generated when passing through each layer of the neural network, and is used for the input of the next layer.
  • the convolutional neural network After passing through all levels, the convolutional neural network will get an estimate of confidence on each classification. If the linear camera obtains image samples under multiple illumination conditions, it can be input to the convolutional neural network separately to obtain multiple confidence evaluation groups.
  • the board identification model trained under certain conditions cannot be accurately classified.
  • the final confidence estimates of category A and category B are approximately equal, for example 51. % vs. 49%, the confidence of this classification is considered to be low.
  • the operator can be notified to intervene by dividing them into a single "unrecognizable classification" or by means of an alarm device.
  • such boards can be processed in an iterative manner. First, it is classified by manual method, and then the image samples of the sample under multi-speed, multi-light, and multi-camera angles are obtained. This results in a new image sample that can be added to the sample used to train the neural network and retrain the neural network when the time is right. By continuously increasing the sample data and training the neural network, the processing accuracy of various abnormal samples can be continuously increased, and the probability of occurrence of low confidence is gradually reduced.
  • An example of the data in the iterative process is given below:
  • Sample N+1, Sample N+2, and Sample N+3 are all corresponding to the same low-confidence board, but correspond to different speed, illumination, camera angle and other attributes. Since the image samples may be affected by the material of the original wood, the image samples of different batches of logs are different. At the same time, even the same batch of logs may change the custom classification and the correspondence between the classification and the sample due to changes in coloring or demand. These rapidly changing needs can quickly adjust the sorting algorithm by changing image samples and retraining the neural network.
  • the training process can be greatly shortened, so that the parameter adjustment, calibration, testing and deployment process that can be completed in a few months can be completed in one day.
  • the above-described wood board recognition method proposed by the present disclosure can accurately classify wood boards regardless of any environment and any speed, and perform classification operations at accurate kick time. Moreover, there is no need for a separate device, such as a photoelectric sensor, to obtain the time of arrival of the board, but the classification time can be determined by classification learning.
  • the board learning device includes a first acquisition module 901, a first splicing module 902, and a training module 903:
  • the first obtaining module 901 is configured to acquire a plurality of sets of one-dimensional images of the wooden board at a plurality of different predetermined speeds; wherein each set of one-dimensional images includes a plurality of one-dimensional images at different positions of the corresponding wooden boards, and each set of one-dimensional Multiple one-dimensional images in the image correspond to the same predetermined speed;
  • the first splicing module 902 is configured to separately splicing each set of one-dimensional images in the acquired plurality of sets of one-dimensional images to obtain a plurality of two-dimensional images at a plurality of different predetermined speeds;
  • the training module 903 is configured to train the board recognition model as the plurality of sets of training data by using the category of the board, the plurality of different predetermined speeds, and the plurality of two-dimensional images; each group of the training data includes the category of the board a two-dimensional image of the plurality of two-dimensional images and a corresponding predetermined speed; the recognition result of the wood board recognition model includes a category of the wood board and a moving speed.
  • the first acquiring module includes:
  • the first acquisition sub-module is configured to acquire a plurality of sets of one-dimensional images of the wood board in a case where the board has a relative speed with the linear camera and the combination of the relative speed and the sampling frame rate of the linear camera corresponds to a plurality of different predetermined speeds.
  • the first acquiring module includes:
  • a second obtaining sub-module configured to acquire a plurality of sets of one-dimensional images of the wood board at a plurality of different predetermined speeds under different lighting conditions
  • Training modules including:
  • the first training sub-module is configured to train the wood board recognition model as a plurality of sets of training data by using a category of the board, a plurality of different predetermined speeds, different illumination conditions, and a plurality of two-dimensional images; each group of the plurality of sets of training data
  • the training data includes the category of the board, a two-dimensional image of the plurality of two-dimensional images, and their corresponding predetermined speeds and lighting conditions.
  • the first splicing module includes:
  • a first grouping sub-module configured to divide each set of one-dimensional images in the acquired plurality of sets of one-dimensional images into at least two groups according to an image acquisition time sequence and/or an image order;
  • the first splicing sub-module is configured to splicing the one-dimensional images of the at least two groups separately to form a plurality of two-dimensional images at different predetermined speeds.
  • the illumination condition includes the intensity of the light of the external light source of the wooden board, the illumination direction of the light of the external light source, the shooting angle of the image acquiring unit that acquires the one-dimensional image, and the aperture size of the image acquiring unit.
  • the predetermined speed is the relative moving speed between the image acquisition unit and the board.
  • the method further includes:
  • the second obtaining module is configured to acquire a plurality of one-dimensional images of the white reference object while acquiring the plurality of sets of one-dimensional images of the wooden board.
  • the first splicing module includes:
  • the third splicing sub-module is configured to splicing each set of one-dimensional images in the plurality of sets of one-dimensional images to obtain a plurality of two-dimensional images;
  • the labeling sub-module is configured to label a plurality of two-dimensional images separately to obtain boundary information of the board.
  • the training module further includes:
  • the second training sub-module is configured to train the board boundary recognition model according to the boundary information of the plurality of two-dimensional images and the wooden board, and the recognition result of the board boundary recognition model includes the boundary information of the board.
  • the machine learning device for the wood board recognition is consistent with the machine learning method for the wood board recognition. For details, refer to the above description of the machine learning method for wood board recognition, and details are not described herein.
  • FIG. 10 is a block diagram showing the structure of a wood board identifying apparatus according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device by software, hardware, or a combination of both.
  • the board identification device includes a third acquisition module 1001, a second splicing module 1002, and an identification module 1003:
  • the third obtaining module 1001 is configured to acquire a plurality of one-dimensional images of the wood board
  • the second splicing module 1002 is configured to splicing the acquired one-dimensional images to obtain a two-dimensional image to be identified;
  • the identification module 1003, the splicing module identifies the two-dimensional image and the trained wood board recognition model, and obtains the category of the board and the moving speed.
  • the second splicing module includes:
  • a second grouping sub-module configured to divide the acquired plurality of one-dimensional images into at least two sets of one-dimensional images according to an image acquisition time sequence and/or an image order;
  • the third splicing sub-module is configured to splicing at least two sets of one-dimensional images to form at least two two-dimensional images to be identified.
  • the identifying module includes:
  • a first identification sub-module configured to input at least two two-dimensional images into the wood board recognition model respectively, to obtain two types of confidence estimates of the category of the board and the moving speed;
  • the first selection sub-module is configured to select the group with the highest confidence from the two sets of confidence estimates as the final recognition result.
  • the method further includes:
  • the kick leg module is configured to obtain the kick timing of the board based on the moving speed of the board.
  • the identifying module includes:
  • a second identification sub-module configured to obtain boundary information of the wooden board according to the two-dimensional image and the trained boundary recognition model
  • the third identification sub-module is configured to obtain the category of the wooden board and the moving speed according to the two-dimensional image, the boundary information, and the wood board recognition model.
  • the identifying module includes:
  • a fourth identification sub-module configured to input a plurality of two-dimensional images obtained under a plurality of different illumination conditions into the wood board recognition model respectively, to obtain a plurality of sets of confidence estimates of the category of the board and the moving speed;
  • the second selection sub-module is configured to select the group with the highest confidence from the plurality of sets of confidence estimates as the final recognition result.
  • the method further includes:
  • the fourth obtaining module is configured to acquire a plurality of one-dimensional images of the white reference object while acquiring the plurality of one-dimensional images of the wood board.
  • the device for identifying the wood board is consistent with the method for identifying the wood board. For details, refer to the description of the board identification, which is not described here.
  • FIG. 11 is a block diagram showing the structure of an electronic device suitable for implementing a machine learning method for wood board recognition according to an embodiment of the present disclosure.
  • the electronic device 1100 includes a central processing unit (CPU) 1101 that can be loaded into a program in a random access memory (RAM) 1103 according to a program stored in a read only memory (ROM) 1102 or a program stored from the storage portion 1108.
  • CPU central processing unit
  • RAM random access memory
  • ROM read only memory
  • the various processes in the embodiment shown in Fig. 1 described above are executed.
  • RAM random access memory
  • various programs and data required for the operation of the electronic device 1100 are also stored.
  • the CPU 1101, the ROM 1102, and the RAM 1103 are connected to each other through a bus 1104.
  • An input/output (I/O) interface 1105 is also coupled to bus 1104.
  • the following components are connected to the I/O interface 1105: an input portion 1106 including a keyboard, a mouse, etc.; an output portion 1107 including a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a speaker; a storage portion 1108 including a hard disk or the like And a communication portion 1109 including a network interface card such as a LAN card, a modem, or the like.
  • the communication section 1109 performs communication processing via a network such as the Internet.
  • Driver 1110 is also connected to I/O interface 1105 as needed.
  • a removable medium 1111 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory or the like is mounted on the drive 1110 as needed so that a computer program read therefrom is installed into the storage portion 1108 as needed.
  • an embodiment of the present disclosure includes a computer program product comprising a computer program tangibly embodied on a readable medium therewith, the computer program comprising program code for performing the machine learning method of the board recognition of FIG.
  • the computer program can be downloaded and installed from the network via the communication portion 1109, and/or installed from the removable medium 1111.
  • the above electronic device can also be used to execute the program code of the wood board identification method in the embodiment shown in FIG.

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Abstract

L'invention porte sur un procédé d'identification de planche de bois, sur un procédé d'apprentissage machine et sur un dispositif d'identification de planche de bois, ainsi que sur un dispositif électronique. Le procédé consiste : à acquérir de multiples groupes d'images unidimensionnelles d'une planche de bois à une pluralité de différentes vitesses prédéfinies, chaque groupe d'images unidimensionnelles comprenant une pluralité d'images unidimensionnelles correspondant à une position différente de la planche de bois et la pluralité d'images unidimensionnelles dans chaque groupe d'images unidimensionnelles correspondant à la même vitesse prédéfinie (S101) ; à associer chaque groupe d'images unidimensionnelles dans les multiples groupes obtenus d'images unidimensionnelles de sorte à obtenir une pluralité d'images bidimensionnelles à différentes vitesses prédéfinies (S102) ; et à former des modèles d'identification de planche de bois respectivement à l'aide des types de planches de bois, de la pluralité de différentes vitesses prédéfinies et de la pluralité d'images bidimensionnelles sous la forme de multiples groupes de données d'apprentissage (S103), chaque groupe de données d'apprentissage dans les multiples groupes de données d'apprentissage comprenant le type d'une planche de bois, l'une des images bidimensionnelles et une vitesse prédéfinie correspondante, et un résultat d'identification d'un modèle d'identification de planche de bois comprenant le type de la planche de bois et la vitesse de déplacement.
PCT/CN2018/109106 2017-12-14 2018-09-30 Procédé d'identification de planche de bois, procédé d'apprentissage machine et dispositif d'identification de planche de bois et dispositif électronique WO2019114380A1 (fr)

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