A dry fiber width detection system

Abstract

The present invention relates to the technical field of carbon fiber production, and particularly to a dry fiber width detection system, which includes the following steps: real-time collecting dry fiber image data and performing preprocessing; segmenting the black area and the blank area in the image data through moving average threshold; applying a Gaussian filter to smooth the image and remove small defects such as fine hairs on the dry fiber part; applying a Laplacian operator for edge detection and extracting the edge of the dry fiber; and transmitting the calculated dry fiber width data to the control center. The dry fiber width detection system can timely extract the edge of the dry fiber and calculate the width, reducing manual intervention and subjective errors. During the system analysis process, operation parameters such as the parameters of the moving average threshold and the Gaussian filter are convenient to adjust, and the operation parameters can be adjusted according to the dry fiber image data collected in different monitoring areas, enhancing the flexibility of the detection system and ensuring the accuracy of dry fiber edge extraction.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon fiber production, and particularly to a dry fiber width detection system. Background Art

[0002] During the production process of dry fibers, the width may become larger or smaller, and it is necessary to detect the width of the dry fibers in real time. Since the measurement accuracy is relatively high and it is inconvenient to observe manually, the method of visual detection is usually used to measure the width of the dry fibers in real time, so as to measure the width change trend of the dry fibers during the production and transportation process.

[0003] When measuring the width of dry fibers by the visual detection method, due to the occurrence of broken filaments or thin filaments in the middle of the dry fiber bundle during the production process, the obtained dry fiber image data is not ideal, and there will be a long blank area in the area belonging to the carbon fiber. When identifying the carbon fiber area by the traditional threshold segmentation method, the extracted carbon fiber area will be inaccurate. Therefore, a detection method that can extract the complete dry carbon fiber area is proposed now to avoid the influence of broken filaments and thin filaments on the accuracy of dry fiber width detection. Summary of the Invention

[0004] The present invention provides a dry fiber width detection system, which can effectively solve the problems in the background art.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A dry fiber width detection system includes the following steps:

[0007] Set a monitoring area and collect dry fiber image data in real time, and preprocess the image data;

[0008] Segment the black area and the blank area in the image data by moving average threshold;

[0009] Apply a Gaussian filter to the threshold-segmented image data to smooth the image and remove small defects such as fluff on the dry fiber part;

[0010] Apply a Laplace operator to the Gaussian-filtered image data for edge detection, and extract the edge of the dry fiber;

[0011] Transmit the calculated dry fiber width data to the control center and compare it with the set warning threshold range.

[0012] Further, during the moving average threshold processing of the image data, first set a sliding window and correspondingly set an initial moving average threshold;

[0013] Next, traverse each pixel point in the image data with the sliding window, and perform binarization processing on the grayscale data of the pixel points according to the moving average threshold;

[0014] Finally, perform morphological opening operation on the binarized image, and then apply morphological closing operation to complete the extraction of the dry fiber region.

[0015] Further, traverse each pixel point of the image data through the sliding window, and place the sliding window centered on the current pixel point;

[0016] Calculate the grayscale average value of all pixel points within the sliding window, compare it with the moving average threshold, set the pixel points below the threshold to black, and set the pixel points above the threshold to white.

[0017] Further, during the process of traversing each pixel point of the image data by the sliding window, dynamically adjust the moving average threshold, specifically using the following formula:

[0018]

[0019] Among them, m represents the moving average threshold of the pixel point at the moving target of the sliding window, k represents the pixel point arrangement serial number, and n represents the number of pixel points occupied by the average dry fiber width.

[0020] Further, set the first corrosion radius r1 and the first dilation radius R1 according to the average distance between two adjacent dry fibers in the image data, and correspondingly set the first corrosion area and the first dilation area;

[0021] Set the corrosion radius r2 and the dilation radius R2 according to the detection accuracy of the dry fibers in the image data, and correspondingly set the second corrosion area and the second dilation area.

[0022] Further, traverse the pixel points at the edge position of the black area in the binarized image with the center of the first corrosion area, and then traverse the pixel points at the remaining edge positions in the black area with the center of the first dilation area to obtain multiple disconnected dry fiber regions;

[0023] Traverse all the pixel points within each dry fiber region with the center of the second dilation area, and then traverse the pixel points at the edge position of each dry fiber region with the center of the second corrosion area to obtain each complete dry fiber region.

[0024] Further, refine the edge of the extracted dry fiber to obtain an edge line with a single-pixel width;

[0025] Pair the edges of the refined edge line to obtain the two sides of each dry fiber, and calculate the distance between the paired edges to obtain the width data of each dry fiber.

[0026] A dry fiber width detection device, comprising:

[0027] A conveying module for synchronously conveying multiple dry fibers;

[0028] A camera module for real-time acquisition of images of passing dry fibers;

[0029] A detection module for processing the acquired dry fiber images and outputting width data.

[0030] An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above dry fiber width detection system is implemented.

[0031] A storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above dry fiber width detection system is implemented.

[0032] The beneficial effects of the present invention are as follows:

[0033] In the present invention, the dry fiber width detection system can collect and process image data of dry fibers in real time, extract the edges of dry fibers in a timely manner and calculate the width, reducing manual intervention and subjective errors. In the system analysis process, operation parameters such as moving average threshold and Gaussian filter parameters are convenient to adjust. The operation parameters can be adjusted according to the dry fiber image data collected in different monitoring areas, enhancing the flexibility of the detection system and ensuring the accuracy of dry fiber edge extraction. Description of the Drawings

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments described in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0035] Figure 1 It is a schematic flow chart of the dry fiber width detection system in the present invention;

[0036] Figure 2 It is a schematic diagram of collecting image data in the monitoring area in the present invention;

[0037] Figure 3 It is a schematic diagram of a dry fiber with broken filaments or thin filaments inside the dry fiber in the present invention;

[0038] Figure 4 It is a schematic diagram of a function for calculating edges using second-order differences in the present invention. Detailed Embodiments

[0039] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0040] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there may also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only implementation.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0042] As Figures 1 to 4 shown, a dry fiber width detection system includes the following steps:

[0043] Set a monitoring area and collect dry fiber image data in real time, and preprocess the image data; segment the black area and the blank area in the image data by moving average threshold; apply a Gaussian filter to the threshold-segmented image data to smooth the image and remove small defects such as fluff on the dry fiber; apply a Laplacian operator to the Gaussian-filtered image data for edge detection, and extract the edge of the dry fiber; transmit the calculated dry fiber width data to the control center and compare it with the set warning threshold range.

[0044] The dry fiber width detection system disclosed by the present invention can collect the image data of dry fibers in real time and perform processing and analysis, extract the edges of dry fibers in time and calculate the width, reducing manual intervention and subjective errors. In the system analysis process, operation parameters such as the parameters of the moving average threshold and the Gaussian filter are convenient to adjust, and the operation parameters can be adjusted according to the dry fiber image data collected in different monitoring areas, enhancing the flexibility of the detection system and ensuring the accuracy of dry fiber edge extraction.

[0045] Among them, when setting the monitoring area, it is necessary to clarify the setting criteria of the monitoring area, such as fixed position, size, or dynamic adjustment according to the actual situation of the production line. Ensure the stability of the image acquisition device to continuously obtain clear dry fiber images. The preprocessing of image data includes image enhancement and noise removal to improve image quality, reduce noise interference, and improve the accuracy of subsequent image segmentation and edge detection.

[0046] In this embodiment, during the moving average threshold processing of image data, first set a sliding window and correspondingly set an initial moving average threshold; secondly, traverse each pixel point in the image data with the sliding window, and perform binary processing on the pixel gray data according to the moving average threshold; finally, perform morphological opening operation on the binary processed image and then apply morphological closing operation to complete the extraction of the dry fiber area.

[0047] During the moving average threshold processing, dynamically adjust the size of the sliding window and the moving average threshold according to the image data collected from different monitoring areas, which can not only meet the image segmentation requirements under different lighting conditions and background noise effects, but also improve the accuracy of segmentation.

[0048] In the specific implementation process, traverse each pixel point of the image data through the sliding window, and place the sliding window centered on the current pixel point; calculate the gray average value of all pixel points within the sliding window, and compare it with the moving average threshold. Set the pixel points lower than the threshold to black and the pixel points higher than the threshold to white.

[0049] Among them, during the process of traversing each pixel point of the image data with the sliding window, dynamically adjust the moving average threshold, and the specific formula is as follows:

[0050]

[0051] Among them, m represents the moving average threshold of the pixel point at the moving target of the sliding window, k represents the pixel point arrangement serial number, and n represents the number of pixel points occupied by the average dry fiber width.

[0052] In the specific implementation process, use the sliding window to scan the pixel points in the image data row by row, calculate an average threshold for each pixel point, and then perform segmentation according to the average threshold of each point. The calculation of the average threshold is determined by the mean value of the gray values of multiple pixels within the sliding window. When the number of scanned pixel points exceeds the yarn width during the movement of the sliding window, the average threshold of the corresponding pixel point is m(k + 1) = m(k) + 1 / n(Zk + 1 - Zk - n).

[0053] Compare the current pixel grayscale value with the average threshold calculated for the corresponding point. If the grayscale value of the current pixel is greater than the average threshold calculated for the corresponding pixel, then change the current grayscale value to white. If the grayscale value of the current pixel is less than the average threshold calculated for the corresponding point, then change the current grayscale value to black.

[0054] In this embodiment, set the first erosion radius r1 and the first dilation radius R1 according to the average distance between two adjacent dry fibers in the image data, and correspondingly set the first erosion area and the first dilation area; set the erosion radius r2 and the dilation radius R2 according to the detection accuracy of the dry fibers in the image data, and correspondingly set the second erosion area and the second dilation area.

[0055] Traverse the pixel points at the edge position of the black area in the binarized image with the center of the first erosion area, and then traverse all the remaining edge position pixel points in the black area with the center of the first dilation area to obtain multiple disconnected dry fiber areas; traverse all the pixel points within each dry fiber area with the center of the second dilation area, and then traverse the pixel points at the edge position of each dry fiber area with the center of the second erosion area to obtain each complete dry fiber area.

[0056] Disconnect the connected carbon fiber bundles through morphological opening operation (erosion first and then dilation), which helps the subsequent separate processing of each dry fiber area. Apply morphological closing operation (dilation first and then erosion) to fill the small blank gaps within the carbon fiber area.

[0057] Apply the Laplace operator for edge detection, and refine the edges of the extracted dry fibers to obtain edge lines with a single-pixel width; perform edge pairing on the refined edge lines to obtain the two side edges of each dry fiber, and calculate the distance between the paired edges to obtain the width data of each dry fiber.

[0058] Among them, Laplace calculates the edge using the second-order difference. As Figure 4 shown, in the case of a continuous function, at the maximum or minimum value in the first-order differential graph, it is considered an edge; at the 0-crossing point between the maximum and minimum values in the second-order differential graph, it is considered an edge.

[0059] A dry fiber width detection device includes: a conveying module for synchronously conveying multiple dry fibers; a camera module for real-time collecting images of the passing dry fibers; a detection module for processing the collected dry fiber images and outputting width data.

[0060] A computer device provided by an embodiment of the present application includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above method is implemented.

[0061] The embodiment of the present application also provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented.

[0062] Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM for short), electrically erasable programmable read-only memory (EEPROM for short), erasable programmable read-only memory (EPROM for short), programmable read-only memory (PROM for short), read-only memory (ROM for short), magnetic memory, flash memory, magnetic disk or optical disk.

[0063] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0064] In the present invention, unless otherwise clearly specified and defined, the terms "mounted", "connected", "coupled", "fixed", etc. shall be construed in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0065] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0066] Any process or method description represented in a flowchart or described in other ways herein can be understood to represent a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a manner other than shown or discussed, including in a substantially simultaneous manner according to the involved functions or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0067] The logic and / or steps represented in a flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in connection with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0068] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0069] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program. The said program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0070] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0071] Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A dry fiber width detection method, characterized in that: The following steps are involved: Setting up monitoring areas and collecting dry fiber image data in real time, and preprocessing the image data; Segment the black and blank areas in the image data by moving average threshold; In the process of moving average threshold processing of image data, a sliding window is first set, and an initial moving average threshold is set accordingly; Secondly, the sliding window traverses each pixel in the image data, and performs binarization processing on the grayscale data of the pixel according to the moving average threshold; Finally, the binary image is subjected to morphological opening operation, and then the morphological closing operation is applied to complete the extraction of the dry fiber area. When the sliding window traverses each pixel of the image data, the moving average threshold is dynamically adjusted, specifically using the following formula: ； Wherein, m represents the moving average threshold of the pixel point at the moving target of the sliding window, k represents the pixel point arrangement number, and n represents the number of pixels occupied by the average dry fiber width. The calculation of the average threshold is determined by the average of the grayscale values ​​of multiple pixels in the sliding window. When the number of scanned pixels exceeds the yarn width during the movement of the sliding window, the corresponding average threshold of the pixel point is ; Apply a Gaussian filter to the thresholded image data; Applying Laplacian operator to the image data after Gaussian filtering for edge detection, and extracting the edge of dry fiber; The edge of the extracted dry fiber is refined to obtain edge lines with a single pixel width; The refined edge lines are edge-paired to obtain the two side edges of each dry fiber, and the distance between the paired edges is calculated to obtain the width data of each dry fiber; The calculated dry fiber width data is transmitted to the control center and compared with the set warning threshold range.

2. The dry fiber width detection method according to claim 1, characterized in that: Traversing each pixel point of the image data through the sliding window, and placing the sliding window with the current pixel point as the center; The grayscale average of all pixels in the sliding window is calculated and compared with the moving average threshold, and the pixels below the threshold are set to black, and the pixels above the threshold are set to white.

3. The dry fiber width detection method according to claim 1, characterized in that: According to the average distance between two adjacent dry fibers in the image data, a first corrosion radius r1 and a first expansion radius R1 are set, and a first corrosion area and a first expansion area are set accordingly; The corrosion radius r2 and the expansion radius R2 are set according to the detection accuracy of the dry fibers in the image data, and the second corrosion area and the second expansion area are set accordingly.

4. The dry fiber width detection method according to claim 3, characterized in that: The center of the first erosion area traverses the pixel points at the edge of the black area in the binary image, and then the center of the first expansion area traverses all the remaining pixel points at the edge of the black area to obtain a plurality of disconnected dry fiber areas; The center of the second expansion area is used to traverse all pixel points in each dry fiber area respectively, and then the center of the second erosion area is used to traverse the pixel points at the edge of each dry fiber area respectively to obtain each complete dry fiber area.

5. A dry fiber width detection device, characterized in that: The dry fiber width detection method according to any one of claims 1 to 4 comprises: A conveying module for synchronous conveying of multiple dry fibers; A camera module, used for collecting images of passing dry fibers in real time; The detection module processes the collected dry fiber images and outputs width data.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the dry fiber width detection method according to any one of claims 1 to 4 is implemented.

7. A storage medium having a computer program stored thereon, characterized in that: When the computer program processor is executed, the dry fiber width detection method according to any one of claims 1-4 is implemented.

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