Seven typical applications of machine vision

With the emergence of "Alpha Dog" and "Alpha Zero", artificial intelligence has once become a topic of discussion after people have had a meal. In recent years, the tide of artificial intelligence development is becoming the main theme of humanity's next stage of development. Many people say that in the era of artificial intelligence, the key is machine vision technology.

Machine vision technology is to use the machine instead of the human eye to do measurement and judgment. Its biggest feature is fast speed, large amount of information and many functions. The machine vision system converts the ingested target into an image signal through machine vision products (ie, image capturing device, divided into CMOS and CCD), and transmits it to a dedicated image processing system to obtain shape information of the target, according to pixel distribution and The information such as brightness and color is converted into a digitized signal; the image system performs various operations on these signals to extract the features of the target, and then controls the action of the device on the basis according to the result of the discrimination.

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As the "eyes" of global intelligence, machine vision has greatly influenced the progress of artificial intelligence. The hot spots in recent hot spots such as unmanned driving, drones and intelligent robots are also premised on the development of machine vision.

As the premise of artificial intelligence development, machine vision technology has seven typical applications:

Image recognition application

Image recognition is the use of machine vision to process, analyze, and understand images to identify targets and objects in various modes. Image recognition The most typical application in the machine vision industry is the identification of two-dimensional codes. A large amount of data information is stored in the two-dimensional code, and the product is tracked and managed through the barcode. Through the machine vision system, the bar code of various material surfaces can be easily recognized and read, which greatly improves the efficiency of modern production.

Image classification application

Whether an image contains an object or not, and characterizing the image is the main research content of object classification. In general, the object classification algorithm globally describes the entire image by manual feature or feature learning method, and then uses the classifier to determine whether there is a certain type of object.

Image classification problem is the task of assigning labels to input images, which is one of the core issues of intelligent vision. This process is often inseparable from machine learning and deep learning.

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Image detection application

Detection is one of the most important applications in the machine vision industry. Almost all products need to be tested, and manual detection has many drawbacks. Therefore, machine vision with many advantages is also very widely used in image detection, for example, in the fifth set of RMB issued in October 2000. The side of the round coin enhances the anti-counterfeiting function. In view of the strict control requirements of the production process, a visual inspection system is installed on the last process of coinage.

Image segmentation application

In the image processing process, it is sometimes necessary to segment the image to extract valuable parts for subsequent processing, such as screening feature points, or dividing a part of one or more pictures containing a specific target.

Image segmentation refers to the process of subdividing a digital image into multiple image sub-regions (a collection of pixels, also referred to as superpixels). The purpose of image segmentation is to simplify or change the representation of the image, making the image easier to understand and analyze. More precisely, image segmentation is a process of tagging each pixel in an image, a process that allows pixels with the same tag to have some common visual characteristics.

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"Image segmentation" is a pixel-level object recognition, that is, each pixel has to judge its category. The difference between it and object detection is that the detection is an object level, only a frame is needed to frame the position of the object, and segmentation is a more difficult problem than detection.

Visual positioning application

Visual positioning requires a machine vision system to quickly and accurately find the part being tested and confirm its position. In the field of semiconductor packaging, devices need to adjust the pickup head according to the chip position information obtained by machine vision, accurately pick up the chip and bind it. This is the most basic application of visual positioning in the machine vision industry.

Object measurement application

The biggest feature of machine vision industrial applications is its non-contact measurement technology, which also has high precision and high speed performance, but non-contact and wear-free, eliminating the potential for secondary damage caused by contact measurement. Common measurement applications include gears, connectors, automotive parts, IC component pins, twist drills, and Luoding thread inspection.

Object sorting application

In fact, the object sorting application is built after the identification and detection, and the image is processed by the machine vision system to realize sorting. It is often used in machine vision industrial applications for food sorting, automatic surface sorting of parts, cotton fiber sorting, etc.