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“See Through” Industrial Production: Machine Vision Enables More Precise Manufacturing
“See Through” Industrial Production: Machine Vision Enables More Precise Manufacturing
25-03-27
Machine vision is a technology that uses computers and digital image processing techniques (image acquisition devices, including CMOS and CCD) to automatically identify, analyze, and process visual information of objects and scenes. It utilizes computers to process image and video data to extract and analyze features such as object shape, color, texture, position, and movement. The target is converted into an image signal, which is then converted into a digital signal based on pixel distribution, brightness, and color information. The image system performs various calculations on these signals to extract the features of the target, thereby achieving object recognition, classification, tracking, measurement, and detection tasks.
I mentioned image processing technology earlier; currently, mainstream image processing techniques include the following.
Digital image processing is the foundation of machine vision, involving the digital acquisition, encoding, transmission, storage, and restoration of images. Digital image processing can perform various operations on images, such as adjusting brightness, contrast, and color balance, filtering noise, edge detection, and binarization, to optimize image quality and extract more accurate information.
Image analysis is a crucial part of machine vision technology, including the analysis of image features such as shape, color, and texture, as well as the extraction and recognition of these features. Image analysis techniques help machines understand the content of images and provide a basis for subsequent decisions.
Image understanding is an advanced stage of machine vision. By combining features extracted through image analysis with technologies such as deep learning, machines can understand the actual content of images. For example, models trained using deep learning algorithms can identify people, objects, and scenes in images.
Pattern recognition is a major branch of image processing. It mainly involves analyzing and processing the characteristics of input data to identify or classify data. In image processing, pattern recognition can be used to identify various patterns in images, such as faces, gestures, and text.
Computer vision is an important branch of machine vision that primarily studies how computers can understand and interpret scenes and objects in the real world through visual information such as images or videos. Computer vision has a wide range of applications, including security monitoring, autonomous driving, and medical diagnosis.
These technologies are interconnected to form a complete machine vision system. With continuous technological advancements, these mainstream technologies are constantly improving and being refined, supporting more applications.
Industrial Machine Vision - Providing Strong Support for Smart Manufacturing
With the development of industrial automation and intelligence, machine vision technology has become increasingly mature and is widely used in industrial manufacturing. As future industrial production continues to advance and transform, machine vision technology will become a key driver. The applications of machine vision technology are extensive, its development trend is positive, and its advantages are significant; it will undoubtedly bring updates and transformations to industrial production in the future.
Deep Learning Empowers Traditional Machine Vision
In the field of industrial measurement, the application of machine vision technology mainly involves aspects such as contour, shape, surface topography, and dimensions. It is commonly used for dimensional and positional detection on production lines, product quality inspection, etc. It is widely used in fields such as casting, sheet metal processing, automotive manufacturing, aerospace manufacturing, and electronics manufacturing. Using machine vision technology to measure parts can greatly improve the detection speed and accuracy of parts, thereby providing strong support for improving production line efficiency and reducing production costs. The four major applications of machine vision include the following areas:
In industrial automation, machine vision technology can be used for automated production on industrial production lines, such as automatic detection, classification, and packaging of products. Machine vision technology can improve the efficiency and quality of work on industrial production lines.
Current industrial production is gradually being taken over by robots, such as multi-joint robotic arms or multi-degree-of-freedom robots. They replace manual labor in industrial production, performing monotonous, frequent, and long-term operations, or working in hazardous or dangerous environments. Harsh environments, such as stamping, die casting, heat treatment, welding, painting, plastic product molding, machining, and simple assembly, are important indicators of the level of automation in modern factories.
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Guangdong Hongiing Optoelectronic Technology Inc.
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