Cloth, gauze, non-woven fabrics and other materials in the modern production line, it is necessary to determine whether the product color is qualified, whether there are impurities and impurities on the surface.
I. Overview of On-line Inspection Technology for Machine Vision Automated Production Machine vision is the use of machines instead of human eyes for measurement and judgment. The machine vision system refers to converting an object to be taken into an image signal by a machine vision product (ie, an image capturing device), transmitting it to a dedicated image processing system, and converting it into a digitized signal according to information such as pixel distribution, brightness, and color; These signals perform various operations to extract features of the target, and then identify the content of the image or control the device action at the scene based on the result of the discrimination. As a high-tech technology for fast, real-time and accurate collection and processing of information, machine vision automatic detection technology has gradually become an indispensable technical tool and means for intelligent informationization and enhancing enterprise competitiveness. Cloth, gauze, non-woven fabrics and other materials in the modern production line, it is necessary to determine whether the product color is qualified, whether there are impurities and impurities on the surface. Due to the fast running speed of the production line, it is required to have small diameters of defects such as impurities and stains. It is difficult to perform real-time detection by manual, and the efficiency of post-sampling inspection is low, and there is still the possibility of flaws in the products after sampling. Machine vision automation is ideal for online, fast, real-time inspection on the production line. The on-line inspection system for the production of cloth, gauze and non-woven fabrics is based on machine vision technology, which quickly and efficiently detects the color of the product and the presence of impurities, stains and the like.
Second, the online testing project requires that in the production process of cloth, gauze, non-woven fabrics and other fabric materials, such as cloth material testing, such as highly reproducible and intelligent work can only be done by manual testing, in the modern assembly line It is often seen later that many inspection workers perform this process, adding huge labor and management costs to the enterprise, while still failing to guarantee a 100% inspection pass rate (ie "zero defect"). The detection of the quality of the cloth is repetitive work, error-prone and inefficient. The assembly line is automatically transformed to make the production line of fabric materials such as cloth, gauze and non-woven fabric into a fast, real-time, accurate and efficient assembly line. On the assembly line, all fabric materials, such as color, and stains, are automatically detected. Machine-based automatic recognition technology is now used to accomplish work that was previously done manually. In the inspection of large quantities of cloth materials, manual inspection of product quality is inefficient and inaccurate, and machine vision inspection methods can greatly improve production efficiency and automation of production. Online Test Project Solution Machine vision uses a computer to process and analyze image information and draw conclusions without human intervention. Machine vision is characterized by automation, objectivity, non-contact and high precision. Compared to image processing systems in general, machine vision emphasizes accuracy and speed, as well as reliability in industrial field environments. In machine vision applications, the following processes are involved: Image Acquisition Through the optical system, images are captured by the camera, and the images are converted to digital format and passed to computer memory. Image Processing The processor uses different algorithms to process image elements that have a significant impact on decision making, such as color recognition, area, length measurement, image enhancement, edge sharpening, and noise reduction. Feature Extraction The processor identifies and quantifies key features of the image, such as the color of the cloth material and the shape of the impurities. This data is then transferred to the control program. Judgment and Control The processor's control program draws conclusions based on the data received. For example: These data include whether the diameter of the impurity is within the required specifications or whether the color of the cloth material is acceptable. The vision system generally includes: a light source, an optical system, a camera, an image processing unit, an image analysis processing software, a monitor, a communication/input and output unit, and the like. The composition of the machine vision system for fabric material inspection is shown in the figure below. The output of the vision system is the result of the processing after the arithmetic processing - the amount of various impurities. After the computer system obtains the detection result in real time, the commanding motion system or the input/output system performs corresponding control actions (such as sorting). Fourth, cloth material detection system proposed detailed program design description: Image processing software machine vision system, visual information processing technology mainly depends on image processing methods, including image enhancement, data encoding and transmission, smoothing, edge sharpening, segmentation , feature extraction, image recognition and understanding. After these processes, the quality of the output image is improved to a considerable extent, which not only improves the visual effect of the image, but also facilitates the analysis, processing and recognition of the image by the computer. Feature Extraction Identification General cloth material detection (automatic identification) first captures standard images with high-definition, high-speed camera lenses, and sets certain standards on this basis; then captures the detected images and compares the two. However, it is more complicated in the quality inspection project of cloth materials: The content of the image is not a single image, and the number, size, color, and position of the impurities present in each of the measured regions are not necessarily the same. 2. The shape of the impurities is difficult to determine in advance. 3. Due to the rapid motion of the cloth material reflecting light, there may be a large amount of noise in the image. 4. On the assembly line, the detection of the cloth material has real-time requirements. For the above reasons, the image recognition processing should adopt the corresponding algorithm to extract the features of the impurities, perform pattern recognition, and realize intelligent analysis. Color Detection In general, images acquired from a color CCD camera are RGB images. That is to say, each pixel is composed of three components of red (R) green (G) basket (B) to represent a point in the RGB color space. The problem is that these chromatic aberrations are different from the human eye. Even small noise changes the position in the color space. So no matter how close our eyes feel, it is not the same in the color space. For the above reasons, we need to convert RGB pixels into another color space CIELAB. The goal is to make our human eye feel as close as possible to the color difference in the color space. Blob Detection According to the processed image obtained above, the impurity stain is detected on a solid background according to the demand, and the area of ​​the excellent spot is calculated to determine whether it is within the detection range. Therefore, the image processing software has the function of separating the target, detecting the target, and calculating the area thereof. Blob Analysis is the analysis of connected domains of the same pixel in an image. This connected domain is called a blob. The stain in the image processed by Binary Thresholding can be considered as a blob. The Blob analysis tool separates the target from the background and calculates the number, location, shape, orientation, and size of the target, as well as the topology between the relevant spots. Instead of using a single pixel to analyze one by one during processing, the rows of the graph are manipulated. Each line of the image uses Run Length Encoding (RLE) to represent adjacent target ranges. This algorithm greatly increases the processing speed compared to pixel-based algorithms. Result Processing and Control The application stores the returned results in a database or user-specified location and controls the mechanical part to move accordingly. According to the result of the identification, it is stored in the database for information management. In the future, the information can be searched and inquired at any time. The manager can know the busyness of the assembly line in a certain period of time, make arrangements for the next work, and can know the quality of the cloth material in the near future. 
4. User Interface and Operation The project requires the use of machine vision technology to intelligently identify all impurities in the fabric material on the assembly line and their quantity and size. According to the project requirements, we design as follows: (1) Image display area: Real-time display of the color image captured by the camera, the system identifies the cloth material information in real time based on the current image content. (2) Information display area: Display the content of the image - the amount of various impurities in real time into the form. The current status of the system (such as: real-time detection, stop detection, trigger signal status) is displayed in the status display column in real time, so that the operator can understand the system status. (3) Information management area: Managers can view the statistics of the pipeline at any time. The operator can flexibly configure the configuration information of the system (such as: database configuration, control module communication configuration, identification parameter correction). The rights management controls the operation rights of the system user. For example, only the advanced operator can configure the system information; only the person with the corresponding authority can view the statistics. Fifth, fabric material color learning tools We have developed a color learning tool, this tool is friendly and easy to operate. Color Learning Tools A color should provide multiple template images for training, which improves recognition. After learning, you should save it as a CLF file, and the pattern recognition will be recognized according to the saved features. VI. Summary The vision system involves optical and image processing algorithms, integrating high-tech technology, especially in the entire identification control system, and also with the motion control system to complete the follow-up operations. The color value of the recognition object is extracted in the visual system of the project, and then the pattern recognition method is used to identify the unqualified area and then use speckle analysis to determine whether it is an impurity. At the same time, the selection of various components in the entire system and the friendliness of the design of the user interface are improved. In short, the application of machine vision systems can significantly reduce inspection costs, improve product quality, and speed up production and efficiency. For modern production enterprises, it is an effective technical means to increase production capacity, achieve high-quality products, and control production costs. Http://news.chinawj.com.cn Editor: (Hardware Business Network Information Center) http://news.chinawj.com.cn
I. Overview of On-line Inspection Technology for Machine Vision Automated Production Machine vision is the use of machines instead of human eyes for measurement and judgment. The machine vision system refers to converting an object to be taken into an image signal by a machine vision product (ie, an image capturing device), transmitting it to a dedicated image processing system, and converting it into a digitized signal according to information such as pixel distribution, brightness, and color; These signals perform various operations to extract features of the target, and then identify the content of the image or control the device action at the scene based on the result of the discrimination. As a high-tech technology for fast, real-time and accurate collection and processing of information, machine vision automatic detection technology has gradually become an indispensable technical tool and means for intelligent informationization and enhancing enterprise competitiveness. Cloth, gauze, non-woven fabrics and other materials in the modern production line, it is necessary to determine whether the product color is qualified, whether there are impurities and impurities on the surface. Due to the fast running speed of the production line, it is required to have small diameters of defects such as impurities and stains. It is difficult to perform real-time detection by manual, and the efficiency of post-sampling inspection is low, and there is still the possibility of flaws in the products after sampling. Machine vision automation is ideal for online, fast, real-time inspection on the production line. The on-line inspection system for the production of cloth, gauze and non-woven fabrics is based on machine vision technology, which quickly and efficiently detects the color of the product and the presence of impurities, stains and the like.
Second, the online testing project requires that in the production process of cloth, gauze, non-woven fabrics and other fabric materials, such as cloth material testing, such as highly reproducible and intelligent work can only be done by manual testing, in the modern assembly line It is often seen later that many inspection workers perform this process, adding huge labor and management costs to the enterprise, while still failing to guarantee a 100% inspection pass rate (ie "zero defect"). The detection of the quality of the cloth is repetitive work, error-prone and inefficient. The assembly line is automatically transformed to make the production line of fabric materials such as cloth, gauze and non-woven fabric into a fast, real-time, accurate and efficient assembly line. On the assembly line, all fabric materials, such as color, and stains, are automatically detected. Machine-based automatic recognition technology is now used to accomplish work that was previously done manually. In the inspection of large quantities of cloth materials, manual inspection of product quality is inefficient and inaccurate, and machine vision inspection methods can greatly improve production efficiency and automation of production. Online Test Project Solution Machine vision uses a computer to process and analyze image information and draw conclusions without human intervention. Machine vision is characterized by automation, objectivity, non-contact and high precision. Compared to image processing systems in general, machine vision emphasizes accuracy and speed, as well as reliability in industrial field environments. In machine vision applications, the following processes are involved: Image Acquisition Through the optical system, images are captured by the camera, and the images are converted to digital format and passed to computer memory. Image Processing The processor uses different algorithms to process image elements that have a significant impact on decision making, such as color recognition, area, length measurement, image enhancement, edge sharpening, and noise reduction. Feature Extraction The processor identifies and quantifies key features of the image, such as the color of the cloth material and the shape of the impurities. This data is then transferred to the control program. Judgment and Control The processor's control program draws conclusions based on the data received. For example: These data include whether the diameter of the impurity is within the required specifications or whether the color of the cloth material is acceptable. The vision system generally includes: a light source, an optical system, a camera, an image processing unit, an image analysis processing software, a monitor, a communication/input and output unit, and the like. The composition of the machine vision system for fabric material inspection is shown in the figure below. The output of the vision system is the result of the processing after the arithmetic processing - the amount of various impurities. After the computer system obtains the detection result in real time, the commanding motion system or the input/output system performs corresponding control actions (such as sorting). Fourth, cloth material detection system proposed detailed program design description: Image processing software machine vision system, visual information processing technology mainly depends on image processing methods, including image enhancement, data encoding and transmission, smoothing, edge sharpening, segmentation , feature extraction, image recognition and understanding. After these processes, the quality of the output image is improved to a considerable extent, which not only improves the visual effect of the image, but also facilitates the analysis, processing and recognition of the image by the computer. Feature Extraction Identification General cloth material detection (automatic identification) first captures standard images with high-definition, high-speed camera lenses, and sets certain standards on this basis; then captures the detected images and compares the two. However, it is more complicated in the quality inspection project of cloth materials: The content of the image is not a single image, and the number, size, color, and position of the impurities present in each of the measured regions are not necessarily the same. 2. The shape of the impurities is difficult to determine in advance. 3. Due to the rapid motion of the cloth material reflecting light, there may be a large amount of noise in the image. 4. On the assembly line, the detection of the cloth material has real-time requirements. For the above reasons, the image recognition processing should adopt the corresponding algorithm to extract the features of the impurities, perform pattern recognition, and realize intelligent analysis. Color Detection In general, images acquired from a color CCD camera are RGB images. That is to say, each pixel is composed of three components of red (R) green (G) basket (B) to represent a point in the RGB color space. The problem is that these chromatic aberrations are different from the human eye. Even small noise changes the position in the color space. So no matter how close our eyes feel, it is not the same in the color space. For the above reasons, we need to convert RGB pixels into another color space CIELAB. The goal is to make our human eye feel as close as possible to the color difference in the color space. Blob Detection According to the processed image obtained above, the impurity stain is detected on a solid background according to the demand, and the area of ​​the excellent spot is calculated to determine whether it is within the detection range. Therefore, the image processing software has the function of separating the target, detecting the target, and calculating the area thereof. Blob Analysis is the analysis of connected domains of the same pixel in an image. This connected domain is called a blob. The stain in the image processed by Binary Thresholding can be considered as a blob. The Blob analysis tool separates the target from the background and calculates the number, location, shape, orientation, and size of the target, as well as the topology between the relevant spots. Instead of using a single pixel to analyze one by one during processing, the rows of the graph are manipulated. Each line of the image uses Run Length Encoding (RLE) to represent adjacent target ranges. This algorithm greatly increases the processing speed compared to pixel-based algorithms. Result Processing and Control The application stores the returned results in a database or user-specified location and controls the mechanical part to move accordingly. According to the result of the identification, it is stored in the database for information management. In the future, the information can be searched and inquired at any time. The manager can know the busyness of the assembly line in a certain period of time, make arrangements for the next work, and can know the quality of the cloth material in the near future. 4. User Interface and Operation The project requires the use of machine vision technology to intelligently identify all impurities in the fabric material on the assembly line and their quantity and size. According to the project requirements, we design as follows: (1) Image display area: Real-time display of the color image captured by the camera, the system identifies the cloth material information in real time based on the current image content. (2) Information display area: Display the content of the image - the amount of various impurities in real time into the form. The current status of the system (such as: real-time detection, stop detection, trigger signal status) is displayed in the status display column in real time, so that the operator can understand the system status. (3) Information management area: Managers can view the statistics of the pipeline at any time. The operator can flexibly configure the configuration information of the system (such as: database configuration, control module communication configuration, identification parameter correction). The rights management controls the operation rights of the system user. For example, only the advanced operator can configure the system information; only the person with the corresponding authority can view the statistics. Fifth, fabric material color learning tools We have developed a color learning tool, this tool is friendly and easy to operate. Color Learning Tools A color should provide multiple template images for training, which improves recognition. After learning, you should save it as a CLF file, and the pattern recognition will be recognized according to the saved features. VI. Summary The vision system involves optical and image processing algorithms, integrating high-tech technology, especially in the entire identification control system, and also with the motion control system to complete the follow-up operations. The color value of the recognition object is extracted in the visual system of the project, and then the pattern recognition method is used to identify the unqualified area and then use speckle analysis to determine whether it is an impurity. At the same time, the selection of various components in the entire system and the friendliness of the design of the user interface are improved. In short, the application of machine vision systems can significantly reduce inspection costs, improve product quality, and speed up production and efficiency. For modern production enterprises, it is an effective technical means to increase production capacity, achieve high-quality products, and control production costs. Http://news.chinawj.com.cn Editor: (Hardware Business Network Information Center) http://news.chinawj.com.cnZirconium Corundum Abrasive Belt
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