A brief discussion on the composition and working principle of machine vision system

Machine vision, also known as computer vision, includes monocular vision, binocular (stereo) vision and multi-viewing vision. It studies the science and technology of using computers to simulate biological external displays or macroscopic visual functions. The primary goal is to use images to create or restore realistic visual models and then perceive the real world.

Applications of Machine Vision

The application of machine vision mainly includes two aspects: detection and robot vision:

⒈ Inspection: It can be divided into high-precision quantitative inspection (such as cell classification in microscopic photos, size and position measurement of mechanical parts) and qualitative or semi-quantitative inspection without measuring instruments (such as product appearance inspection, parts identification and positioning on the assembly line, defect detection and assembly completeness inspection).

⒉ Robot vision: It is used to guide the robot's operation and actions in a large range, such as picking up workpieces from a pile of messy workpieces sent out by a hopper and placing them on a conveyor belt or other equipment in a certain orientation (i.e., the hopper picking problem). As for operations and actions in a small range, tactile sensing technology is also needed.

In terms of industry applications, the main industries include pharmaceuticals, packaging, electronics, automobile manufacturing, semiconductors, textiles, tobacco, transportation, logistics, etc. Using machine vision technology to replace manual labor can improve production efficiency and product quality. For example, in the logistics industry, machine vision technology can be used to sort and classify express delivery, which will not happen in most express delivery companies. It will reduce the damage rate of items, improve sorting efficiency, and reduce manual labor.

Machine Vision Features

⒈The camera's shooting speed automatically matches the speed of the object being measured to capture the ideal image;

⒉The size of the parts ranges from 2.4mm to 12mm, and the thickness can be different;

⒊ The system calls the corresponding visual program to perform size detection and output the results according to the workpieces of different sizes selected by the operator;

4. For parts of different sizes, the sorting device and conveying device can accurately adjust the width of the material channel, so that the parts move on a fixed path and undergo visual inspection;

5. The resolution of the machine vision system reaches 2448×2048, and the dynamic detection accuracy can reach 0.02mm;

6. The scrap missed detection rate is 0;

⒎This system can monitor the detection process by displaying images, and can also dynamically view the detection results through the detection data displayed on the interface;

⒏ It has the function of sending out rejection control signals to wrong workpieces in time and accurately, and rejecting waste products;

⒐The system can self-check whether the status of its main equipment is normal, and is equipped with status indicator lights; at the same time, it can set different operation permissions for system maintenance personnel and users;

⒑Real-time display of the inspection screen, Chinese interface, can browse the images of unqualified products several times, with the function of storing and viewing the images of wrong workpieces in real time;

⒒Can generate error result information file, including corresponding error image, and can print out.

Composition of machine vision system

As the name suggests, a machine vision system consists of three main parts: machine, vision, and system.

The machine is responsible for mechanical movement and control; vision is realized through light sources, industrial lenses, industrial cameras, image acquisition cards, etc.; the system mainly refers to software, and can also be understood as a complete set of machine vision equipment.

Image acquisition: light source, lens, camera, acquisition card, mechanical platform

Image processing and analysis: industrial control host, image processing and analysis software, graphical interactive interface.

Judgment execution: Telex unit, Mechanical unit

Input device for machine vision

Including: Imaging equipment - scene detection imaging

Digitizing equipment - converting analog signals output by imaging equipment into two-dimensional/three-dimensional digital images

Commonly used imaging equipment: CCD color/black and white cameras, digital scanners, ultrasonic imaging detectors, CT imaging equipment, infrared imaging systems, laser imaging systems, computational imaging systems, etc.

The optical band used by the observation system can range from visible light, infrared, X-rays, microwaves, ultrasound to gamma rays depending on the physical medium, material and state of the detection object.

Observation objects: static, moving, flat, stereo high-definition

How machine sense works

The machine vision inspection system uses a CCD camera to convert the target to be inspected into an image signal, which is then transmitted to a dedicated image processing system. Based on information such as pixel distribution, brightness and color, it is converted into a digital signal. The image processing system performs various operations on these signals to extract the characteristics of the target, such as area, quantity, and degree. Then, according to the preset tolerance and other conditions including size, angle, number, qualified/unqualified, presence or absence, etc., an automatic recognition function is realized.

The biggest feature of machine vision system

Use various knowledge - feature models, imaging models, object models, relationships between objects, etc., to perform automatic calculations as much as possible at all stages of vision. The system should use knowledge that can be clearly expressed. When the system has higher adaptability and robustness, it can also solve more difficult problems in machine vision.