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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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How to choose usb3.0 industrial camera 2026-08-18 .gtr-container-f8d7e6 { 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%; } .gtr-container-f8d7e6 p { font-size: 14px; margin-bottom: 1em; text-align: left !important; } .gtr-container-f8d7e6 .gtr-intro-highlight { font-weight: bold; color: #0000FF; font-size: 18px; display: block; margin-bottom: 15px; } @media (min-width: 768px) { .gtr-container-f8d7e6 { padding: 30px 40px; max-width: 960px; margin: 0 auto; } } High-definition USB industrial camera is an industrial camera using USB3.0 interface. Compared with USB2.0 industrial cameras, USB3.0 industrial cameras not only have the consistent flexibility and ease of use of USB2.0, but also have a transmission rate that is nearly 10 times higher. Because the USB3.0 interface is faster, the image data captured by the USB3.0 industrial camera can be transmitted to the host in real time, enabling high-speed, real-time visual inspection. The USB interface is simple and easy to implement and has strong compatibility, which makes USB have a very high application penetration rate around the world. The cost of the interface driver chip and driver circuit is low, so the overall cost of USB3.0 industrial cameras is low. USB 3.0 continues the ease of use and flexibility of USB2.0. It is plug-and-play, requires no external power supply, and does not need to install a capture card. USB 3.0 has a backward compatibility standard, is compatible with USB1.1 and USB2.0, and supports future optical fiber transmission. Industrial cameras are a key component in machine vision systems. Their most essential function is to convert optical signals into ordered electrical signals. Choosing an appropriate camera is also an important step in the design of a machine vision system. The camera not only directly determines the resolution and quality of the images collected, but is also directly related to the operating mode of the entire system.
How to choose an industrial camera brand 2026-08-18 /* Unique root container class */ .gtr-container-k7p2q9 { font-family: Verdana, Helvetica, "Times New Roman", Arial, sans-serif; color: #333333; line-height: 1.6; padding: 20px; max-width: 100%; box-sizing: border-box; } /* General paragraph styling */ .gtr-container-k7p2q9 p { font-size: 14px; margin-bottom: 15px; text-align: left !important; /* Enforce left alignment for paragraphs */ } /* Styling for the question/introductory text before the list */ .gtr-container-k7p2q9 .intro-question { font-size: 16px; font-weight: bold; color: #0000FF; /* Theme color for emphasis */ margin-top: 25px; margin-bottom: 20px; text-align: left; } /* Ordered list styling */ .gtr-container-k7p2q9 ol { list-style: none !important; /* Remove default list markers */ padding: 0; margin: 0; counter-reset: list-item; /* Initialize the counter for the list */ } .gtr-container-k7p2q9 ol li { position: relative; padding-left: 35px; /* Space for the custom number */ margin-bottom: 25px; counter-increment: none; /* Increment the counter for each list item */ list-style: none !important; /* Ensure no default marker */ } /* Custom number for ordered list items */ .gtr-container-k7p2q9 ol li::before { content: counter(list-item) "." !important; /* Display the counter value */ position: absolute !important; left: 0 !important; top: 0; font-size: 16px; font-weight: bold; color: #0000FF; /* Theme color for numbers */ width: 25px; /* Fixed width for number alignment */ text-align: right; } /* Styling for the title within each list item */ .gtr-container-k7p2q9 .list-item-title { font-size: 18px; /* Max allowed font size for titles */ font-weight: bold; color: #0000FF; /* Theme color for titles */ margin-bottom: 8px; display: block; /* Ensure it takes full width */ } /* Paragraphs within list items */ .gtr-container-k7p2q9 ol li p { margin-top: 0; margin-bottom: 0; } /* Responsive adjustments for PC screens */ @media (min-width: 768px) { .gtr-container-k7p2q9 { padding: 30px 40px; max-width: 960px; /* Max width for better readability on large screens */ margin: 0 auto; /* Center the component */ } .gtr-container-k7p2q9 p { margin-bottom: 20px; } .gtr-container-k7p2q9 .intro-question { font-size: 18px; margin-top: 30px; margin-bottom: 25px; } .gtr-container-k7p2q9 ol li { margin-bottom: 30px; padding-left: 40px; } .gtr-container-k7p2q9 ol li::before { font-size: 18px; width: 30px; } .gtr-container-k7p2q9 .list-item-title { font-size: 20px; /* Slightly larger for PC */ margin-bottom: 10px; } } Industrial Camera Brand Company is a high-tech enterprise specializing in the research, design, development and promotion of optics, visual lighting, and electromechanical products. We have a rich professional team for optical and opto-mechanical system design, new product development and testing. We can design, produce samples and arrange mass production according to customer engineering requirements. Our quality management team ensures the best product quality. High-speed capture is one of the advantages of industrial camera brands over ordinary civilian cameras, and it is also an important application. As the core component of machine vision systems, industrial cameras play an irreplaceable role in industry applications. According to different industry applications, we need to choose different types of industrial cameras to ensure that the machine vision system can maximize its functional advantages. What aspects should you start with when choosing a suitable high-speed snapshot camera: Accuracy meets requirements The screening of this requirement has nothing to do with high-speed capture and is similar to the selection of ordinary industrial cameras, so I won’t go into details here. Determine color requirements To capture the color characteristics of an object, you must use a camera with good color reproduction, such as a high-quality CCD or 3CCD camera. Projects that have nothing to do with color generally use black-and-white cameras, but this is not entirely the case. Some features to be detected may be better displayed in color pictures. In this case, you may also consider using a color camera. Exposure time, how to shoot moving objects This part is the focus of discussion. When shooting moving objects, the most important problem that needs to be overcome is smear. Smear is caused by the relative movement between the shooting target and the camera system during exposure. This relative movement causes the image formed on the chip to keep changing. The pixels in each part are affected by the imaging from different positions of the object during the exposure process. The final picture is a superposition of pictures in a continuously changing image space. As long as the object is moving, there will be smear. In order to prevent it from having a significant impact on the detection, different project types have different restrictions on the relative length of the smear. For dimensional measurement projects, smear will have a serious impact on the measurement accuracy. In this case, industrial cameras Brands will require that the trailing length be as short as possible, such as no more than 1/3 pixel, or no more than one pixel, etc. For projects related to identification, counting, etc., the requirements for trailing will be relatively wider. These requirements are generally the case, not absolute, and will depend on the actual situation.
Application of CCD industrial cameras 2026-08-18 .gtr-container-f3g4h5 { 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-f3g4h5 p { font-size: 14px; margin-bottom: 1em; text-align: left !important; word-break: normal; overflow-wrap: normal; } .gtr-container-f3g4h5 .gtr-intro-paragraph-f3g4h5 { font-size: 14px; margin-bottom: 1.5em; color: #444444; } .gtr-container-f3g4h5 .gtr-section-f3g4h5 { margin-bottom: 24px; padding: 16px; border-left: 4px solid #0000FF; background-color: #F0F0FF; border-radius: 4px; box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05); } .gtr-container-f3g4h5 .gtr-section-title-f3g4h5 { font-size: 18px; font-weight: bold; color: #0000FF; margin-bottom: 1em; padding-bottom: 0.5em; border-bottom: 1px solid #D0D0FF; } .gtr-container-f3g4h5 .gtr-subsection-f3g4h5 { margin-top: 16px; margin-bottom: 16px; padding-left: 16px; border-left: 2px solid #A0A0FF; } .gtr-container-f3g4h5 .gtr-subsection-title-f3g4h5 { font-size: 16px; font-weight: bold; color: #4D4DFF; margin-bottom: 0.8em; } .gtr-container-f3g4h5 ul, .gtr-container-f3g4h5 ol { margin: 0; padding: 0; list-style: none !important; } .gtr-container-f3g4h5 li { position: relative; padding-left: 20px; margin-bottom: 0.5em; font-size: 14px; text-align: left !important; list-style: none !important; } .gtr-container-f3g4h5 ul li::before { content: "•" !important; position: absolute !important; left: 0 !important; color: #0000FF; font-size: 1.2em; line-height: 1; } .gtr-container-f3g4h5 ol { counter-reset: list-item; } .gtr-container-f3g4h5 ol li::before { content: counter(list-item) "." !important; position: absolute !important; left: 0 !important; color: #0000FF; font-weight: bold; width: 18px; text-align: right; } @media (min-width: 768px) { .gtr-container-f3g4h5 { padding: 24px; } .gtr-container-f3g4h5 .gtr-section-f3g4h5 { padding: 24px; margin-bottom: 32px; } .gtr-container-f3g4h5 .gtr-subsection-f3g4h5 { padding-left: 24px; } } The early application scope of image processing technology of CCD industrial cameras was affected by the high price and slow processing speed of image processing equipment, and was limited to certain fields. Since the 1970s, with the development of theory and the advancement of computer technology due to the integrated circuit revolution, the application scope of image processing has gradually broadened. The initial application of CCD industrial cameras in the transportation field. In 1978, the Federal Highway Administration in the United States commissioned EE Hilbert, C. Carl, W. Gross, GR Hanson, MJ Olasaby and AR Johnson to develop a wide-area detection system. Other countries such as Japan, France, the United Kingdom, and Sweden have also successively invested in considerable research and achieved good results. Compared with foreign countries, the development of domestic image processing technology applied to transportation has made considerable progress in recent years. For example, license plate recognition is currently very popular in China, and many manufacturers have launched corresponding products. The following is a brief introduction to the application of image processing technology in transportation, which is divided into three parts: vehicle detection, vehicle type identification, and vehicle tracking. 1), vehicle detection Vehicle detection methods can be roughly classified into sample point detection, detection line detection, and full-screen detection. In addition, nighttime vehicle detection is explained as follows: (1), Sample point detection Select a matrix-like sample point in a certain part of the lane. When a vehicle passes by, the gray level value of the sample point is different from that of the original road surface. If the statistical value of the subtraction between the two exceeds a certain threshold, it indicates the presence of the vehicle. (2), detection line detection This method is to lay out virtual detection lines composed of pixels in the direction of vertical or parallel traffic flow, as shown in Figure 2. It is generally composed of bright spots to facilitate the separation of the pixel depth of the road surface and the detection line. When a vehicle passes the detection line, the gray level value on the line is different from when no vehicle passes the road; if the difference in gray level value is greater than a certain threshold, it means that a vehicle has passed. Since the sample point or detection line detection method only captures part of the pixel data for processing, the amount of data processed is significantly reduced, so the calculation time is shortened a lot. In order to meet the requirements of real-time detection, the image processing system AUTOSCOPE, which is currently used in traffic detection, uses detection lines for processing. On road sections where vehicles are running simply, good results can be obtained by using sample points or detection lines as a means of vehicle detection; however, in complex intersections, how to lay out sample points or detection lines will be the first problem encountered, because vehicles in intersections not only go straight, but also have turning behaviors, and vehicles may appear at any position. (3), full-screen detection The vehicle detection method that uses full screen processing can obtain more information, but the amount of data to be processed is also significantly increased. There are two detection methods that belong to this method: background subtraction method and binarization method: the background subtraction method takes an image without a vehicle as the background. When the image containing the vehicle is subtracted from the background image point by point, the part of the vehicle is subtracted, such as the TRIP system. The binarization method cuts the image at a certain threshold. The pixel depth higher than this value becomes 255 (white), and the pixel depth lower than this value becomes 0 (black). This can separate the object from the background. Both the background subtraction method and the binarization method have many shortcomings. The former requires frequent updating of the background, while the latter has a complicated process. The common disadvantage of both is that when the color of the object is similar to the background, it will face the fate of cutting failure. In addition, it is difficult to establish the threshold value, so the multi-valued cutting method is proposed, but the process becomes increasingly complicated. (4) Vehicle detection at night Foreign R. Taktak, Rita Cucchiara, Cucchiara and others believe that because the information contained in nighttime images is quite different from that of daytime images, the use of algorithms and detection streams will be quite different. Generally speaking, at night and under low lighting conditions, the only eye-catching visual features are car headlights and their beams, street lights, and highly reflective light patterns (such as zebra crossings). They believe that nighttime images are not suitable for motion detection algorithms. 2), vehicle identification (1), vehicle identification Due to the difference in traffic composition between domestic and foreign countries, foreign studies only identify large vehicles and small vehicles, while domestic studies are more complicated. However, general research simplifies vehicle types into large vehicles, small vehicles and locomotives, and uses these three categories for identification. When using detection lines or sample points as a way to identify vehicle types, because the amount of data collected is small, it is not conducive to vehicle type identification, so there are fewer people using this method. For vehicle recognition in daytime images, vehicle characteristics such as appearance and size are usually used as classification criteria. Relevant literature is organized as follows. "Neural Network", which has developed rapidly in recent years and has been applied to many fields, has also been applied to the identification of vehicle types. In addition, the vehicle license plate can also be used to binarize the number image, and identify and record the license plate number through feature matching. Through database comparison, each number can correspond to a certain vehicle type, which can be used to capture illegal vehicles, vehicle counting, vehicle type identification, origin and destination point investigation, travel time analysis, etc. (2) License plate recognition License plate recognition technology has become increasingly mature in the country in recent years. Some scholars believe that license plate recognition can be divided into three stages: pre-processing, which binarizes the image and removes noise. Then, for license plate positioning, the connecting component labeling method is used to find the connecting components in the image for analysis, and then determine the license plate position. For character recognition, after character segmentation, the size of the structural components is set according to the text size, and finally the typological method is used to find text features for comparison. Some scholars use other methods, such as searching for the license plate and then using pixel segmentation to cut the characters and then use neural networks to identify the characters; or use the calculation of gray-scale conversion numbers to find possible license plate positions, then segment the characters, and then use stroke analysis to identify the characters; or use the changing characteristics of the gray-scale values ​​in the image to find the license plate location, use vertical projection histograms to segment the characters, and use gray-scale value correlation for recognition. 3), vehicle tracking The recording of vehicle trajectories in continuous images is called tracking. Anthony P. Ciervo first proposed the method of detecting vehicles and predicting the vehicle's position to continuously track the trajectory of the vehicle. Among them, those who use sample points or detection lines for tracking are in a passive state because the selected pixels are only limited to a fixed range, which is not conducive to tracking. N.Hoose takes a T-shaped intersection with two-lane two-lanes in each adjacent direction as an example. Transverse detection line shields are laid out on the lanes before entering the intersection and after leaving the intersection to record the adjacent direction numbers of vehicles entering and leaving. At the same time, the shape, size and position of the vehicle on the image are recorded to track the vehicle, but the error is quite large. In summary, there are four vehicle tracking methods: pattern-based tracking, area-based tracking, and contour-based tracking. In foreign research literature, only a single vehicle in each lane is tracked. To understand the mechanism of vehicle operation in an intersection, it is necessary to track vehicles in all directions within the intersection at the same time. Otherwise, only incomplete information will be obtained, which will be of limited help to the whole.
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