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China Shenzhen City Haozhou Technology Co., Ltd.
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Shenzhen City Haozhou Technology Co., Ltd.
Shenzhen Haozhou Technology Co., Ltd. was established in 2014. It is a high-tech company specializing in R&D, design, production, sales, CMOS camera module, USB camera module, analog camera module, endoscope camera module, sensor chip and other high-quality camera modules technology enterprise. Provide a full range of processes, SMT, modules, assembly, packaging and other one-stop services. Passed IS09001 CE ROHS quality system certification. Products are widely used in nearly a hundred fields such as face recognition, biometrics, artificial intelligence, machine vision, drones, self-service terminals, smart homes, security monitoring, and medical applications.Warmly welcome OEM and ODM. We can design according to the drawings provided by customers. All of our products have a 2-year warranty, we are willing to provide customers with products that carry corporate culture and convey brand ideas to end users, we believe that success is built on a solid foundation and commitment to delivery. haozhou provides high quality camera modules with professional OEM design and manufacturing services to customers worldwide. We are carrying OmniVision, Sony, Samsung, Hynix, GalaxyCore... The main application areas: AI VR mobile phone, digital still camera, laptop, DV, PDA/handheld, toy, PC camera, security camera, automotive camera, tablet pc, visual doorbell, medical system, smart home, industrial image, recognition system, fingerprint identification system ...
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A brief analysis of the application of machine vision in laser marking 2026-08-02 .gtr-container-x7y2z1 { font-family: Verdana, Helvetica, "Times New Roman", Arial, sans-serif; color: #333; line-height: 1.6; padding: 20px; box-sizing: border-box; max-width: 100%; margin: 0 auto; } .gtr-container-x7y2z1 p { font-size: 14px; margin-bottom: 1em; text-align: left !important; color: #333; } @media (min-width: 768px) { .gtr-container-x7y2z1 { max-width: 800px; padding: 40px; } } Machine vision usually refers to the process in which a machine automatically obtains an image of interest, then processes the image, and takes further "actions" after obtaining abstract information. In layman's terms, machine vision is to give machines the ability to "see", so that the machine can recognize and understand what it wants to see, and determine the location of the target it sees. Through cameras and computers, it frees the human eye and allows the machine to automatically measure and identify targets. With the advancement of production technology, laser marking machines are facing new requirements and challenges. The area of ​​the devices that need to be marked is getting smaller and smaller, and the accuracy requirements are getting higher and higher. The representative one is IC (integrated circuit) technology in microelectronics technology. Its integration level is getting higher and higher, the speed is getting faster, and the volume is getting smaller and smaller. SMD devices have gradually developed into mainstream IC products, and their appearance is characterized by miniaturization. The application of laser marking machines in the semiconductor industry is mainly for marking the surface of IC chips, marking the manufacturer, model, production batch and other information on the surface of the IC chip. The marking machines on the market now usually mark fixed areas, and the equipment is basically not equipped with a visual observation system, let alone a fine-tuning function during the marking process. After the computer sets the marking content, the device needs to be placed within the marking area, and sometimes manual adjustment is required. This method has very low accuracy and is only suitable for applications in industries that are not sensitive to mark position deviations, such as the production number of beverage bottles, the number of light rail tickets, and personalized markings on the back cover of mobile phones. After using machine vision, it can not only clearly mark information on the chip surface, but also has dynamic adaptability, and can automatically correct the chip orientation error to ensure marking accuracy. In addition, the application of machine vision in laser marking equipment can not only improve the quality of the equipment, but also increase the marking speed to meet the needs of the rapid development of the modern semiconductor industry. Machine vision is applied to production equipment and is one of the key technologies for automated production. It can improve production efficiency, improve product quality, and reduce production costs. At present, machine vision is developing rapidly. With the progress of society, machine vision will gradually develop and improve and be applied to more fields.
Problems with machine vision technology 2026-08-02 .gtr-container-j8k2m1n3 { font-family: Verdana, Helvetica, "Times New Roman", Arial, sans-serif; color: #333333; max-width: 100%; margin: 0 auto; padding: 15px; box-sizing: border-box; } .gtr-container-j8k2m1n3 p { text-align: left !important; font-size: 14px; line-height: 1.6; margin-bottom: 1em; } .gtr-container-j8k2m1n3 .gtr-problem-list { list-style: none; padding-left: 0; margin: 1.5em 0; counter-reset: list-item; } .gtr-container-j8k2m1n3 .gtr-problem-list li { list-style: none !important; position: relative; padding-left: 30px; margin-bottom: 1.5em; } .gtr-container-j8k2m1n3 .gtr-problem-list li::before { content: counter(list-item) "." !important; position: absolute !important; left: 0 !important; color: #0000FF; font-weight: bold; width: 25px; text-align: right; top: 0; line-height: 1.6; } .gtr-container-j8k2m1n3 .gtr-list-item-title { font-weight: bold; font-size: 16px; color: #0000FF; margin-bottom: 0.5em; } @media (min-width: 768px) { .gtr-container-j8k2m1n3 { max-width: 800px; padding: 30px; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.08); border-radius: 8px; } } Machine vision mainly studies the use of computers to simulate human visual functions to extract information from images of objective things, process and understand them, and ultimately use them for actual detection, measurement and control. In recent decades, machine vision technology has been widely used in agriculture, industry, medicine and other fields due to its outstanding advantages such as non-contact, fast speed, high precision, and strong on-site anti-interference ability. As long as objects need to be identified, characterized, and detected, machine vision can flex its muscles and complete the task quickly and well. However, we have to admit that machine vision still has shortcomings in practical applications. For the human eye, it may be easy to recognize an object and understand the surrounding scene, but for machine vision, there are the following problems: An image has multiple meanings When a three-dimensional image is projected into a two-dimensional image, a lot of information is lost. Perhaps two identical two-dimensional images are projected from two completely different three-dimensional scenes. In addition, the same object, photographed from different visual angles, will also produce different images, which will be very difficult for machines to distinguish. Influence of environmental factors Surrounding noise, light instability, the shape and color of the object, and changes in the mutual position of the camera and the object will all have a certain impact on the image. The amount of data is relatively large Whether it is a color image or a gray image, the amount of information is very large, so it requires a lot of storage space, which makes it difficult to process quickly. Real-time issues Real-time is mainly divided into hard real-time and soft real-time. With the development of hardware, there are better solutions to hard real-time problems. Soft real-time problems are mainly solved by algorithms to improve image processing speed. However, in complex work sites, there will be problems of reduced accuracy and robustness. Therefore, it is impossible to obtain an accurate image of the object. After decades of development, machine vision has become increasingly mature. This has greatly improved the level of machine automation and intelligence, and brought huge economic benefits to society. Although there are still certain difficulties in solving problems such as image resolution and real-time performance, with the development of science and technology, these problems will be well solved. Machine vision is also an emerging industry in our country. More and more companies and scientific research institutions are beginning to study machine vision. Machine vision will inevitably become a pillar industry in our country.
A brief analysis of industrial robot visual positioning technology 2026-08-02 .gtr-container-rbtvps { font-family: Verdana, Helvetica, "Times New Roman", Arial, sans-serif; color: #333333; line-height: 1.6; padding: 16px; box-sizing: border-box; max-width: 100%; overflow-x: hidden; } .gtr-container-rbtvps p { font-size: 14px; margin-bottom: 1em; text-align: left !important; } .gtr-container-rbtvps .gtr-heading-level1 { font-size: 18px; font-weight: bold; color: #0000FF; margin-top: 2em; margin-bottom: 1em; padding-bottom: 0.5em; border-bottom: 1px solid #E0E0FF; } .gtr-container-rbtvps .gtr-caption { font-size: 13px; color: #666666; text-align: center; margin-top: 1em; margin-bottom: 2em; } .gtr-container-rbtvps ol { list-style: none !important; padding-left: 25px; margin-bottom: 1em; } .gtr-container-rbtvps ol li { position: relative; margin-bottom: 0.8em; font-size: 14px; text-align: left !important; } .gtr-container-rbtvps ol li::before { content: counter(list-item) "." !important; position: absolute !important; left: -25px !important; font-weight: bold; color: #0000FF; width: 20px; text-align: right; } .gtr-container-rbtvps ol li .list-item-title { font-weight: bold; color: #0000FF; display: inline; } .gtr-container-rbtvps ol li p { display: inline; margin: 0; } @media (min-width: 768px) { .gtr-container-rbtvps { padding: 24px 40px; max-width: 960px; margin: 0 auto; } .gtr-container-rbtvps .gtr-heading-level1 { font-size: 20px; margin-top: 2.5em; margin-bottom: 1.2em; } .gtr-container-rbtvps p { margin-bottom: 1.2em; } } Robot integrates electronic technology, sensing technology and intelligent control technology. It is a machine device that can automatically perform work tasks. It can accept human command and can also act independently in accordance with the principles and programs formulated by artificial intelligence technology. It has been applied in many fields. At present, industrial robots can only perform predetermined instructions in a strictly defined structured environment and lack the ability to perceive and adapt to the environment, which greatly limits the application of robots. The workpiece visual positioning method, combined with dedicated image processing software, utilizes the robot's visual control without the need for pre-teaching or offline programming of the industrial robot's motion trajectory. It can achieve reliable positioning of the industrial robot's visual system and play an active role in improving workpiece positioning accuracy and processing effects. It can save a lot of programming time and improve production efficiency and processing quality. In China, this aspect is mainly used in welding robot tracking of weld seams. 1. Composition of visual positioning system The robot visual positioning system is composed of (as shown in the figure below). A spraying tool and a single camera are installed at the end of the articulated robot so that the workpiece can completely appear in the camera's image. The system includes camera system and control system: (1) Camera system: It consists of a single camera, a computer and a collection system (including an image capture card), responsible for the collection of visual images and machine vision algorithms; it is recommended to use a digital camera for this system, and the extraction accuracy will be higher than that of a general camera. (2) Control system: composed of a computer and a control box, used to control the actual position of the robot end; The work area is photographed by a CCD camera, and the computer extracts tracking features through image recognition methods for data recognition and calculation. Figure 1 Composition of the visual positioning system of the spraying robot 2. Working principle of visual positioning system The American TEO brand digital camera TM-C6597E is used, equipped with a TM-C520HP image acquisition system. It is designed with a dedicated camera, balanced transmission line, and image acquisition card to input the video signal into the calculator and process it quickly. First, select a local image of the object being tracked. This step is equivalent to the process of offline learning, establishing a coordinate system in the image and training the system to find the tracking object. After learning, the image card continuously collects images, extracts tracking features, performs data recognition and calculation, solves the given position values ​​of each joint of the robot through inverse kinematics, and finally controls the high-precision end actuator to adjust the position of the robot. In this way, the visual positioning system combines area-based matching and shape feature recognition for data recognition and calculation, and can quickly and accurately identify the boundaries and centers of object features. The robot control system obtains the angle error of each joint position of the robot through inverse kinematics solution, and finally controls the high-precision end actuator to adjust the robot's posture to eliminate this error. This solves the problem that the actual position of the robot end is far from the desired position, and improves the positioning accuracy of traditional robots. 3. Image feature extraction The contrast between the workpiece on the workbench and the background of the workbench forms a big difference in color, that is, the workpiece is identified as black, and the center line of the black image is extracted. The acquisition system uses this information as an important feature for identifying the workpiece. When a general vision system acquires a workpiece image, it cannot be used directly in the vision system due to various conditions and noise interference. Image preprocessing such as grayscale correction and noise filtering must be performed through image analysis and recognition.
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