What are the common aberrations of machine vision lenses?
In addition to the color difference between the polychromatic light, the lens could be divided into five types of aberrations, namely spherical aberrations, coma aberrations, aberrations, field curvatures, and distortions that affected the similarity of the images. The spherical aberration was caused by the different condensing abilities of the points on the lens surface of the lens. The converging points of the near-axis light and the far-axis light were different. The converging light formed a circle of dispersion that was symmetrical with the optical axis as the center, resulting in blurred images. It was related to the size of the aperture. The image was clearer when the aperture was small, and the image was blurred when the aperture was large. Coma aberrations were aberrations caused by off-axis imaging. After parallel light rays emitted from a point outside the optical axis passed through the optical system, they formed an symmetrical diffused spot on the image plane, shaped like a comet. Its size was related to the aperture and field of view. Aspect was also a type of off-axis aberrations that was only related to the field of view. Read more exciting novels for free
What is machine vision for?
Machine vision was an inspection machine equipped with sensory visual instruments (such as autofocus cameras or sensors). The proportion of optical inspection instruments was very high. Its effects were as follows: 1. ** Detection **: - It was used to detect the defects of various products. - Nearly 80% of industrial vision systems were mainly used for inspection, including improving production efficiency, controlling product quality, collecting product information, etc. The product classification and selection were also integrated into the inspection function. The vision system checks the products on the production line to determine whether they meet the quality requirements. According to the results, the corresponding signal is input to the host computer. If a non-qualified product is found, the alarm will alert and exclude it from the production line. The result is the source of quality information for the CAQ system, which can also be integrated with other CIMS systems. 2. ** Location **: - Calibrating and positioning materials on an automated production line. - The machine vision system can locate the position and direction of a machine (such as an unmanned truck), ensure that the machine is on the correct path, and transmit the position and direction data to the controller in real time to perform the task. 3. ** Simulating human vision **: - Using a computer to realize the human visual function, that is, to identify the objective three-dimensional world. The mechanism was to use optical systems, cameras, and image processing software to simulate human visual abilities, make corresponding decisions, and finally complete the decision through the execution device in the command machine. 4. ** Special applications **: - In some special application scenarios, it can effectively avoid personal safety risks, or it can be used to replace artificial vision in situations where artificial vision is difficult to meet the requirements. It also avoided direct contact between the detection system and the inspected item, preventing damage to the item and contamination of the clean room. There was also no maintenance and cost of mechanical parts wear and tear. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
What are the applications of machine vision?
The application of machine vision is very extensive, mainly reflected in the following aspects: 1. ** Industry **: - ** Quality inspection **: It can inspect the size, shape, and surface defects of parts to ensure product quality and integrity, such as parts inspection in car manufacturing. It could also measure the appearance and size of products on the production line, detect industrial defects, etc. For example, the DLIA industrial defect detection system could adapt to various types of identification and detection tasks, accurately identify and classify various defects in products. - ** Assembly Line Monitor **: Monitor the operation of the assembly line, improve production efficiency, and reduce labor costs. - ** Robot Guidance **: It allows the robotic arm to have vision, achieve precise grabbing, automatic sorting, and other operations. It can also imitate human movements to complete various tasks, improve production efficiency and reduce costs. It will show flexibility and adaptability in the fields of logistics, storage, and intelligent manufacturing. - ** Flow/operation optimization **: With the help of machine vision, robots or other machinery can perform operations in new ways or complete tasks that could not be completed before. For example, in the new rubber grinding solution, the process of grinding complex rubber-like materials can be automated and the operation time can be shortened. 2. ** Medical field **: It is used for medical image analysis, surgery assistance, and pathological detection. For example, it can help doctors analyze medical images such as CT and MRI, identify tumors or other diseases, and improve the accuracy of diagnosis. 3. ** Security Surveillance Domain **: - ** Public safety **: Through high-resolution cameras and image processing algorithms, real-time monitoring of specific areas, identifying suspicious behavior or objects, and improving public safety. - ** Security monitoring **: Able to identify suspicious behaviors and abnormal events through real-time monitoring and analysis of video streams. - ** traffic monitoring **: Real-time analysis of traffic flow, vehicle speed, license plate recognition and other information, so that the traffic management department can better dispatch traffic resources and reduce traffic congestion. 4. ** Autopilot field **: It is one of the frontiers of autonomous driving technology. Through sensors such as cameras and lidars, the autonomous driving system can obtain real-time environmental information, identify pedestrians, vehicles, and traffic signs, and thus achieve safe driving. For example, the complex MV system equipped with the automatic car service vehicle of Waymo One is composed of multiple lidars, radars, cameras, and AI software. It can collect sensor data and calculate the best route in real time. 5. ** Logistics field **: For example, the size of trays can be marked. The machine vision system can also be used to identify and read the barcodes (such as QR codes, barcodes, etc.) on the surface of various materials, greatly improving the production efficiency of the assembly line. It can be used for product tracking management. In the field of food packaging industry, it can identify and trace the QR code to solve the problems of food data integration, random inspection, and tracking. 6. ** Agriculture and environmental monitoring **: It is used for crop monitoring, pest identification, etc. Farmers can take timely measures to increase crop yield through the analysis of farmland images. 7. ** Vision positioning and cutting **: For example, the visual positioning of insoles, using a blade cutter to complete the trimming of the insoles. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
The specific application of machine vision
Machine vision has a wide range of specific applications in many fields: - ** Electronic and semiconductor production **: The usage demand accounts for more than 50% of the total machine vision demand. It is mainly used for 3C surface inspection, AOI optical inspection, printed circuit board, electronic packaging, screen printing, semiconductor alignment and identification, and other high-precision manufacturing and quality inspection. For example, in the production of smart phones, a mobile phone needed to go through at least 70 sets of machine vision inspection systems before it could leave the factory. - ** Car manufacturing **: Contribution to sales of machine vision technology is around 15%. It was used in the manufacturing process of almost all systems and components in the car manufacturing industry, such as body assembly inspection, panel printing quality inspection, character inspection, precision measurement of part size, surface defect inspection, free-form surface inspection, gap inspection, etc. In an automated car production line, it was common for a car manufacturing line to be equipped with more than ten sets of machine vision inspection systems. - ** Pharmaceutical manufacturing field **: Used for packaging defect detection, pill missing detection, production date printing detection, pill color identification and sorting, etc. However, its coverage in pharmaceutical manufacturing had not yet reached expectations. In the future, most pharmaceutical production lines would be equipped with more than five sets of machine vision systems. - ** Food packaging field **: mainly used in appearance packaging inspection, food packaging defect inspection, appearance and internal quality inspection, etc. Currently, it is mainly used in large food production enterprises, and most small food production enterprises have not used it due to cost constraints. - ** Logistics and intelligent manufacturing **: When machine vision is combined with robotic arms, the robotic arms can achieve precise grabbing, automatic sorting, and quality inspection. They can also use algorithms to imitate human movements to complete various tasks, improve production efficiency, reduce costs, and demonstrate flexibility and adaptability. - ** Print industry **: For example, in high-precision operations such as ink-jet printing and laser printing, Haikang's machine vision system can accurately identify and locate printed materials to ensure printing accuracy. It can also detect defects, mispositions, and defects in printed products in real time through deep learning algorithms to reduce the reject rate. It can also be combined with automated equipment to realize intelligent sorting and packaging of printed products. - ** Other fields **: In the field of Internet + safety production, it can analyze the behavior of dangerous chemicals loading and unloading workers; in the food industry, it can be applied to high-speed bag sorting and packaging; in the field of semiconductor, it can realize online quality inspection and sorting of products; in the automated production line, it can help various machines collect visual data, analyze and perform specific tasks. It can also be applied in logistics, transportation, agriculture, tobacco, medical and other fields. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
Multipliers of the lens, machine vision
In the machine vision lens, there was no content that specifically mentioned the difference between the calculation of the lens multiple and the calculation of the general lens multiple. According to the calculation method of the camera lens multiple, the lens multiple refers to the ratio of the large focal length and the small focal length of the lens. The calculation method is to divide the large focal length of the lens by the small focal length to obtain the lens multiple. The lens magnification was one of the important indicators that affected the performance of the lens. The larger the magnification, the larger the zooming range of the lens. It was suitable for different shooting scenes, but it also affected the aperture and image quality of the lens. When choosing a lens, one needed to choose a lens that suited one's needs and budget. <a href="/?from=ask_words" style="color:red" target="_blank">Read more exciting novels for free</a>
Machine vision navigation lens
The following is some of the lens related information that may be involved in machine vision navigation: In the field of machine vision navigation, the lens was an important part of the machine vision system. For example, in the application of binoculars (often used for environmental perception of driverless vehicles, robot navigation and obstacle avoidance, etc.), the camera needed to be equipped with a suitable lens to obtain images, and then the depth information of the scene was restored through the calculation of disparity. From the information provided on industrial lenses, such as the Fa lens of the Haikang Robot industrial camera,(For example, MVL-RHF0824M-10MP 8mm fixed-focus lens, MVL-KF1628M-12MPE 16mm fixed-focus lens, etc.). These lenses have high-definition resolution, LCD visual detection, low distortion, high resolution, and high contrast. They can be used for machine vision detection. In machine vision navigation scenes, If visual detection is needed to assist navigation (such as detecting and identifying objects on the navigation route), such lenses can be used. There was also the KINCENT lens, such as the 3.6mm distortion-free lens equipped with the high-speed 60-frame USB3.0 global shutter 3D depth camera. This lens could be used for machine vision 3D reconstruction. It could be used when navigation needed to build a 3D environment map to achieve better path planning and obstacle avoidance. In addition, lenses of different specifications such as the MPY series and MPZ series lenses of the colorspace Compass Computar (such as the V0828 -MPY2 with an aperture of 1.1 inches and 12 million pixels, and the V0826 - MPZ with a 1-inch and 20 million pixels) could also be applied to the image acquisition part of the machine vision navigation system according to the different requirements of the machine vision navigation system for image quality, resolution, focal length, etc. <a href="/?from=ask_words" style="color:red" target="_blank">Read more exciting novels for free</a>
Top 10 Machine Vision Firms in China
The rankings of China's top ten machine vision companies are as follows: 1. jintuo shares 2. precision electronics 3. saiteng shares 4. Meiya Optoelectronics 5. matrix technology 6. LEAD 7. Kanghong Intelligent 8. Huaxing yuanchuang 9. Tianzhun TZTE 10. Shenzhen keda These companies had strong strength and market share in the field of machine vision. They had made remarkable achievements in technological innovation and market expansion, and showed strong competitiveness in the domestic and foreign markets. The products and solutions of these companies were widely used in various industries, including industrial manufacturing, security monitoring, logistics and express delivery. Their technology and products have made important contributions to the development of China's machine vision industry.
What is the future of machine vision engineers?
The future of machine vision engineers was full of opportunities. With the upgrading of the domestic industry and the advancement of smart manufacturing, the machine vision market is expected to continue to maintain rapid growth. The arrival of the Industry 4.0 era and the integration of advanced technologies such as the Internet of Things and big data opened up a broader application prospect for machine vision. The demand for visual engineers increased year by year, especially in cities such as Beijing, Shanghai, and Suzhou. With the development of artificial intelligence and machine learning technology, visual engineers were widely used in fields such as image processing, computer vision, and natural language processing. In addition, visual engineers were also widely used in industries such as the Internet, games, and smart hardware. According to statistics, the average salary of visual engineers in the past two years was as high as 23.3K per month, and the highest was 30K-50K. In general, a machine vision engineer was a career with broad prospects and high salaries.
The latest machine vision camera selection
The selection of a machine vision camera requires consideration of the following aspects: ** 1. Type of camera ** 1. ** Line Array Industrial Cameras ** - It was suitable for capturing continuous, long production lines. For example, detecting problems on the roller, or scenes that required a large field of view or extremely high precision. It worked with an extremely long and narrow imaging feature, and the field of view was a slender strip. 2. ** Array camera ** - It was more suitable for capturing the entire scene. 3. **3D camera ** - It is suitable for specific scenarios that require 3D information acquisition. For example, in the 3D machine vision camera market research, it will involve the market size (production capacity, output, sales, output value, price, cost, profit, etc.) and industry competition analysis (raw materials, market applications, product types, market demand, market supply, downstream market analysis, supply chain analysis, major companies, market share, M & A, expansion, etc.). ** 2. Signal Type ** 1. ** Simulation camera ** - An image acquisition card was required. The resolution of a standard simulated camera was very low, usually 768 x 576, and the frame rate was fixed (25 frames per second). The data collected was an analogy signal, which was converted into a digital signal by the digital acquisition card for transmission and storage. However, it is susceptible to distortion caused by electromagnetic noise interference from other equipment in the factory (such as electric motor or high-voltage cable). As the noise level increases, its dynamic range (the ratio of the original signal to noise) will decrease, affecting the amount of information transmitted to the computer. 2. ** Digital camera ** - What was collected was a digital signal that was not affected by electromagnetic noise. The dynamic range was higher, and it could transmit more accurate signals to the computer. ** 3. Determination ** The resolution of the camera was chosen according to the system's requirements. For example, to detect the scratches on the surface of an object, the size of the object required to be shot was 10mm × 8mm, and the required detection accuracy was 0.01mm. Assuming that the field of view to be shot was 12mm × 10mm, then the camera that should be chosen had a minimum resolution of (12/0.01)×(10/0.01) = 1200x1000, which was about 1.2 million pixels. Generally, the lowest resolution camera required was determined according to the actual accuracy inspection requirements. The common camera had 1.3 million resolution to choose from. ** 4. Chip Type ** 1. ** Charge Coupled Device (CCD-Chip) camera ** - If the object to be shot is moving and the object to be processed is also moving in real-time, it is more suitable to choose a camera with a CCD-chip. 2. ** Complimentary Metal Oxide Silicon (LCD) chip camera ** - If the object being photographed was moving very slowly, the distance of the object's movement was very small within the set exposure time of the camera, which was converted to a 1 - 2-pixel-sized image, then the use of a LCD camera was also suitable (if not for measurement). Moreover, if the manufacturer's LCD camera used the frame exposure method, it could also be used as a CCD. However, if the deviation was more than 2 px (caused by the movement of the object), the image taken by the object would have smear, so it was not suitable to choose a LCD camera. ** 5. Other Key Elements ** The frame rate, color depth, dynamic range, and sensitivity of the camera were key factors in the selection. The quality of the lens, the optical format of the camera, the number of graphics, and the type of interface (such as USB, ether, or GigE) were also considerations that could not be ignored when selecting the camera. At the same time, for the line array industrial camera, parameters such as line frequency, line height, and trigger method also needed to be considered. The line frequency was the camera's shooting speed, and the line height was based on the application requirements to determine how many lines to capture an image (that is, the image's "high" resolution). The trigger methods were line trigger (suitable for scenes where the motion speed was not uniform or required some special imaging technology, such as long and short exposure HVR, time-sharing stroboscopic, etc.) and frame trigger (suitable for shooting objects with relatively uniform motion. After the frame trigger, the line array camera would continue to shoot at the set line frequency until it reached the specified line height). <a href="/?from=ask_words" style="color:red" target="_blank">Read more exciting novels for free</a>
Is learning machine vision easy to find a job?
Learning machine vision can help you find a job. Computer vision and industrial vision were one of the most demanding fields in the current market. For fresh graduates, the job market was more tolerant. They did not need relevant work experience. As long as they had basic skills, they could find a job. The job prospects in the machine vision industry were good, but the work could be hard, requiring adjustments at the customer's site and frequent business trips. In addition, learning machine vision also required certain skills and knowledge, such as deep learning and image processing. Therefore, learning machine vision can increase the chances of finding a job, but the specific employment situation still needs to be evaluated according to individual ability and market demand.
Ranking of domestic machine vision leading companies
The leading companies in the domestic machine vision industry were ranked as follows: 1. jintuo shares 2. precision electronics 3. saiteng shares 4. Meiya Optoelectronics 5. matrix technology 6. LEAD 7. Kanghong Intelligent 8. Huaxing yuanchuang 9. Tianzhun TZTE 10. Shenzhen keda These companies had strong strength and market share in the field of machine vision. Please note that this ranking may not be unique. Other sources may have different results.