In the industrial sector, machine vision technology has been integrated into industrial automation systems to replace traditional manual inspection, thereby enhancing both product quality and production output. From applications such as pick-and-place operations and object tracking to measurement and defect detection, visual data is leveraged to achieve closed-loop control, ultimately improving the overall system performance.

Although machine vision inspection systems can effectively improve inspection efficiency, reduce labor costs, and lower production expenses, they also have certain limitations during use.TrickyThe issue.
For example, many factories and enterprises purchase testing equipment and software, but due to the lack of experience among operators, minor issues frequently arise during the production process, thereby affecting the effectiveness of the testing equipment.
Many factors can affect imaging stability, such as the surrounding environment, changes in objects, and the impact of visual hardware, among others..
Today, Xiao Ju has compiled a list of factors that affect the performance of visual inspection equipment. We hope this will be helpful to everyone.

1.Hardware Selection Stable extraction of image features is a prerequisite for image analysis and image recognition. Reliable transmission of images to the image-processing center ensures the accuracy of software-based image processing. Selecting hardware for visual inspection is a challenging task that requires engineers to have an in-depth understanding of both the hardware itself and its suppliers, as well as sufficient experience in component selection. After all, the operating environments of machine vision systems used in laboratories differ significantly from those encountered in real-world operational settings. Visual inspection involves several key components, such as light sources, lenses, cameras, image acquisition cards, data transmission systems, image-processing software, and measurement tools. As the performance of these individual components improves, the functionality of machine vision systems increases exponentially. The complexity of the system depends on the specific application requirements. Therefore, when selecting the optimal components, it’s essential not only to ensure that their performance meets the necessary specifications (such as resolution, frame rate, and measurement algorithms) but also to take into account the ultimate environmental conditions in which the system will operate. For example, in industrial settings, these environmental conditions may include component changes, relocation, positioning, handling interfaces, vibration, ambient light, temperature, dust, oil, water, electromagnetic radiation, and more. Under extremely harsh conditions, it may sometimes be necessary to provide additional protection for machine vision components. A typical example is that certain cameras need to be used in relatively clean environments. However, under normal circumstances, industrial environments can generally accommodate direct use of industrial cameras. Nevertheless, even the most stable visual inspection systems often fail to deliver satisfactory results due to external influences. For instance, vibration can cause image blurring and distortion; variable lighting conditions may lead to inconsistent image quality; and prolonged exposure times can degrade the clarity of moving objects in the captured images.

2.Environmental impact
The impact of the environment on machine vision hardware not only damages the hardware itself but also affects measurement results.

Temperature
Temperature variations can also affect the performance of machine vision inspection. Cameras are typically marked with a specified operating temperature range when they leave the factory. Most industrial cameras can operate between -5°C and 65°C; temperatures that are too cold or too hot can impair the camera's normal operation.
For example, when the exposure time is longer or the ambient temperature is higher, the internal temperature of the camera rises. This temperature increase causes the circuitry to generate dark current, which is a major source of noise in image sensors. Studies have shown that for every 8°C rise in the temperature of a CCD chip, its dark current increases exponentially.
On the other hand, the object being measured may also be affected by temperature changes—after all, we know that many materials expand when heated and contract when cooled. Consequently, when measuring such objects, their length and volume will undergo changes.

Vibration
Vibration can cause image blurring and distortion. However, most industrial cameras are equipped with vibration-damping features.
Robots and orbital cables enable the camera to move smoothly without being affected by vibrations. Locking connectors protect the camera from vibration-induced disturbances. The durable PC and embedded computer feature robust stability protection mechanisms. The focal-length lenses employ metal-interface locking screws to ensure they remain unaffected by vibrations. This filter provides a certain degree of protection for the lens.

Ambient lighting
The performance of machine vision inspection is affected by ambient lighting. External illumination can influence the total light intensity incident on the object being inspected, thereby increasing noise in the image data output.
Daily-use optical filters can, to a certain extent, mitigate the effects of ambient light and alter the light information entering the sensor. By employing a high-brightness modulated light source, reducing the sensor’s exposure time, and narrowing the aperture, the impact of ambient light can be minimized. Using infrared cameras or similar devices for measurement can further reduce the influence of visible light.

Dust, dirt, water
Many cameras can achieve an IP 65/67 protection rating to ensure dust and water resistance. Dust, dirt, liquids, and vapors often accumulate on the surface of LEDs or lenses, impairing image quality.
It can be adjusted by increasing the camera’s gain, processing images with software, and adjusting the LED output.

Background
The image background has a significant impact on object detection.
For example, suppose an object is placed on a sheet of paper, and an image of the same object is printed onto that paper. In this scenario, the machine vision inspection setup might not be able to determine which one is the real object. A perfect background is one that is blank and provides good contrast with the detected object; its exact characteristics will depend on the specific visual inspection algorithm being used. If an edge detector is employed, the background should not contain any distinct lines. Additionally, the background’s color and brightness should differ from those of the object.

Obstruction
Occlusion means that part of an object is obscured.
In the previous scenarios, the entire object was visible in the camera image. Occlusion is different, as part of the object becomes invisible; naturally, a visual system cannot detect something that isn't present in the image. A wide variety of factors can cause occlusion, including other objects, parts of the robot itself, or improper positioning of the camera. Approaches to overcoming occlusion typically involve matching the visible portions of the object with its known model and assuming that the hidden parts of the object are still present.

Electromagnetic interference
Electromagnetic interference is an unavoidable disturbance factor in industrial inspection environments.
The switching on and off of motors, transformers, and capacitor components can cause inrush currents, EFT (electrical fast transient) pulse disturbances, and significant electromagnetic radiation. Moreover, the movement of industrial equipment can also lead to issues such as electrostatic discharge (ESD), including air-gap discharge or contact discharge. Industrial cameras, being image sensors themselves, operate on low voltages and are therefore particularly vulnerable to these effects. Consequently, it is essential to implement robust circuit protection measures.
Therefore, various environmental factors can all affect imaging, and these effects can be critical—after all, images serve as crucial references for measurements. Consequently, when visual inspection equipment is purchased, the choice of operating environment becomes extremely important. It’s not enough simply to obtain on-site counts; a favorable environment is also beneficial for equipment installation. Proper installation helps ensure that the equipment’s service life remains within normal limits.