Research on machine vision began in the 1950s with pattern recognition of two-dimensional images. It was originally designed to replace the human eye in detection and recognition tasks, significantly improving efficiency and reducing inconsistencies caused by eye fatigue. Today, machine vision inspection has advanced to the point where it can perform tasks difficult for the human eye, and it can even detect objects invisible to human vision using infrared, ultraviolet, and X-ray detection technologies. However, the challenge in designing machine vision systems lies in ensuring their reliability and stability. The design of hardware such as light sources and cameras, as well as image processing software, all significantly impact the stability of machine vision systems.
Introduction to Imaging Systems and Instability Factors
The imaging system mainly consists of a camera (CCD/CMOS), a lens, and a light source. It is the foundation of visual inspection. The purpose of the imaging system is to acquire qualified raw images. A good imaging system must ensure the stability of image quality during system operation. Stable image acquisition is the basic guarantee of the stability of visual inspection.
1. The impact of industrial cameras on imaging stability
For vision system designers, the selection of industrial cameras mainly considers their sensor type, resolution, and frame rate. Sensors are divided into two types: CCD and CMOS. CMOS image sensors have high integration, and the distance between various components and circuits is very close, resulting in more severe interference and high imaging noise. Compared with CMOS cameras, CCD sensor cameras have the characteristics of high sensitivity, low noise, and fast response speed. In terms of stability, CCD cameras are also more resistant to shock and vibration. Generally speaking, CCD sensor cameras are superior to CCD cameras in terms of image quality and stability.
Another important factor affecting camera image quality is the camera lens. In addition to selecting appropriate parameters such as focal length, depth of field, and aperture according to specific working conditions, a significant factor affecting the system's detection accuracy is geometric distortion error. This is the inherent perspective distortion of optical lenses, which is affected by the manufacturing process and cannot be eliminated, only compensated for. Although many industrial cameras now use various methods to compensate for errors caused by lens distortion, geometric distortion will still affect detection accuracy in high-precision detection fields.
2. The influence of light source on imaging stability
Light sources amplify image features and defects, reduce clutter and background noise, and directly impact the quality of input data. Due to the lack of universal lighting equipment, light source design has always been a challenge for machine vision systems. Typically, it's necessary to select the light source type for each specific application, and also to carefully consider the light source installation and illumination method based on the specific environment to achieve optimal results. Different types of light sources exhibit varying stability. Common visible light sources include LEDs, halogen lamps, fluorescent lamps, and sodium lamps. The biggest drawback of visible light is its inability to continuously and stably output light energy. For example, fluorescent lamps experience a 15% decrease in light energy within the first 100 hours of use, with the output continuing to decline over time. Besides visible light, in high-intensity inspection scenarios, invisible light sources such as X-rays and ultrasound are often used as light sources, providing continuous and stable light energy output. However, these are less conducive to the operation of the inspection system and are expensive. The non-uniformity of the light source also affects image quality; differences in luminous intensity in different directions can also introduce noise. LED light sources in the visible light spectrum offer better stability and lifespan compared to halogen lamps and fluorescent lamps, with shorter response times, customizable colors, and lower operating costs, leading to their widespread application. Illumination methods for these light sources can be categorized as backlighting, front lighting, structured light lighting, and strobe lighting, with the design principle being to highlight image features.
3. Software stability
The impact of detection software stability on machine vision is undeniable. The vision system will ultimately use software on a computer to perform a series of image processing tasks such as image filtering, edge detection, and edge extraction. Different image processing and analysis methods, as well as different detection methods and calculation formulas, will bring different errors. The quality of the algorithm determines the level of measurement accuracy.
4. Environmental factors
The measurement environment in which a vision system operates includes factors such as temperature, lighting, power supply variations, dust, humidity, and electromagnetic interference. A good environment is essential for the normal operation of the vision system. External lighting affects the total light intensity illuminating the object being measured, increasing noise in the image data output. Changes in power supply voltage can also cause instability in the light source, generating noise that varies over time. Temperature variations also affect camera performance; cameras are labeled with their normal operating temperature range at the factory, and both overheating and undercooling can affect their normal operation. Electromagnetic interference is an unavoidable factor in industrial inspection environments. It has a particularly severe impact on low-voltage circuits such as industrial camera circuits and data signal transmission circuits. Qualified vision products undergo rigorous anti-interference testing at the factory, greatly reducing the impact of external electromagnetic interference on the hardware circuitry.
5. Impact of mechanical structure positioning
Besides the imaging system hardware, the relative positional relationship between the camera and the object also affects the stability of image quality. For example, vibrations in the mechanical support structure of the camera or workpiece can affect detection accuracy, and this is a difficult problem to troubleshoot. When inspecting a workpiece dynamically, the impact of motion blur on image accuracy needs to be considered (blurred pixels = object speed * camera exposure time). Furthermore, ideally, the optical axis of the CCD camera lens should be perpendicular to the plane of the workpiece. However, in actual use, due to installation errors or manufacturing errors of the camera or workpiece, the optical axis cannot be guaranteed to be perfectly perpendicular to the measured plane, resulting in a certain angular deviation, which also affects measurement accuracy.
