In recent years, the application of machine vision system has been very extensive, and the high precision, high detection efficiency and low production cost of machine vision system have been favored by enterprises. No matter how good things will also have problems, machine vision system in the use of the process, due to the influence of various factors, the measurement accuracy will be reduced, to the enterprise inconvenience.

There are many factors that affect the measurement accuracy of machine vision inspection equipment, including the brightness, uniformity and directivity of the light source will directly affect the quality and clarity of the image, affecting the measurement accuracy.

Lens resolution, distortion, lens quality, and correction all affect image sharpness and distortion, which in turn affects measurement accuracy. Parameters such as the camera's resolution, frame rate, dynamic range, noise level, and color accuracy affect the quality and stability of the image, which in turn affects the measurement accuracy. Ambient light, temperature, humidity and other factors will affect the quality and stability of the image, and directly affect the accuracy of the measurement. Different image processing algorithms will affect the measurement results, and selecting the appropriate algorithm can improve the measurement accuracy.

Correct camera calibration and system calibration are critical to measurement accuracy, and incorrect calibration and calibration can lead to measurement errors. The reflection, color, texture and other characteristics of the object surface will affect the quality and sharpness of the image, and then affect the accuracy of the measurement.

The stability and accuracy of the mechanical structure of the machine vision system will affect the measurement accuracy, and mechanical vibration or deformation will affect the accuracy of the measurement results. The resolution and magnification of the camera will affect the capture and display of details in the image, and directly affect the accuracy of the measurement.

The accuracy of data acquisition, processing and analysis is also an important factor affecting the measurement accuracy, and the error of data processing will directly affect the accuracy of the measurement results.

By optimizing the above factors, the measurement accuracy of machine vision inspection equipment is improved to ensure that it can be accurately and reliably measured and tested in various application scenarios.

Visual inspection equipment hardware selection

In terms of choosing the hardware of the visual inspection equipment, choose the camera suitable for the application scenario, considering the resolution, frame rate, sensor size, sensitivity and other parameters. Common camera types include CCD and CMOS.

The right lens type and focal length to capture the desired image detail. Consider the lens quality, distortion correction and other factors. Suitable lighting system to provide sufficient intensity and uniformity of light source, improve image quality and detection accuracy.

Consider using dedicated image processing hardware, such as GPU accelerators, to speed up image processing and improve system performance.

Ensure the stability and accuracy of the mechanical structure to avoid vibration or deformation affecting the measurement accuracy. Suitable calibration and calibration equipment to ensure that the system can accurately calibrate and calibrate, improve the measurement accuracy.

Consider the needs of data storage and transmission, appropriate interfaces and storage devices to ensure efficient transmission and safe storage of data. Adaptability in different environmental conditions, such as temperature, humidity and other factors, with good environmental adaptability of the hardware. The extensibility of the equipment makes it easy to upgrade and expand the function and performance of the system when needed.

Considering the performance and price of the equipment, select cost-effective hardware equipment to meet the budget and performance requirements.

Visual inspection equipment software problems

In the software of visual inspection equipment, suitable image processing algorithms are selected, such as edge detection, feature extraction, target recognition, etc., in order to achieve accurate detection and measurement. Consider whether you need to apply machine learning or deep learning techniques to improve the accuracy and robustness of your inspection equipment.

An intuitive and user-friendly interface is designed to make it easy for operators to use the equipment for inspection and analysis. With efficient data processing and analysis functions, it can quickly process a large amount of image data and extract the required information.

If real-time detection and feedback are required, ensure that the software can achieve rapid response and processing to meet real-time requirements. Calibration and calibration tools are provided to help users calibrate and calibrate the system to ensure the accuracy of measurements.

Design appropriate data storage and management systems to ensure the security and traceability of data and facilitate subsequent analysis and review. Consider whether you need to support remote access and control features so that users can monitor and manage devices remotely. Ensure that the software has a good update and maintenance mechanism, timely fix bugs and provide new features, maintain the stability and performance of the system.

If it is necessary to integrate with other systems or devices, ensure that the software has a good interface and compatibility to facilitate data exchange and communication between systems.