How to improve the stability of the visual system?

Today, as industrial automation continues to upgrade towards high-precision and high-beat development, machine vision systems have become a core component of production line quality control and intelligent manufacturing. However, in actual applications, vision systems often face unstable factors from the environment, electrical interference, mechanical vibration and other aspects, resulting in problems such as reduced image acquisition quality, unstable recognition rate, and abnormal data transmission, which directly affect production line efficiency and detection accuracy.

How to build a stable and reliable vision system? The following will conduct a systematic analysis from multiple dimensions such as environmental adaptability, structural protection, light source matching, and data interface, which also provides a practical reference for the stable operation of the vision system.

1. Environmental adaptability: Challenges of temperature, humidity, dust, water, and vibration

Industrial sites are often accompanied by high temperature, high humidity, dust, water vapor and even oil mist, which puts higher requirements on the structural design of traditional cameras and lenses.

In order to cope with complex environments, many industrial cameras have begun to support IP67 protection, reaching high standards in dust and water resistance, and are suitable for metal processing, automobile manufacturing, food sorting and other scenarios. In addition, through metal shell reinforcement, shockproof structure and special mounting parts, image offset or loose connection caused by vibration shock can be greatly reduced.

Practical application:

In the high-speed patch production line of the 3C electronics factory, we deployed a batch of industrial cameras and anti-vibration lenses with IP67 grade, which have been running stably for more than one year, and the system image deviation rate has dropped by more than 90%.

2. Data transmission and interface reliability: high speed, anti-interference, and long distance are the key

After the image acquisition is completed, efficiently and reliably transmitting the data back to the upper system is the key to ensuring the recognition effect. Currently, commonly used interfaces include USB3.0, GigE, 10GigE, CoaXPress, etc., which need to be flexibly selected for different needs.

For example, in scenarios with large image volume and high frame rate, it is recommended to use 10GigE or CXP interfaces to meet high-bandwidth transmission requirements; in long-distance wiring scenarios, GigE is more stable when paired with a gigabit switch. In addition, some cameras also support PoE power supply and data synchronization, which greatly simplifies wiring and improves system stability.

Technical suggestions:

Using industrial connectors with locking structures and anti-interference shielded cables can effectively avoid image interruptions caused by loose interfaces or electromagnetic interference.

3. Light source and algorithm matching: the invisible key to improving image consistency

Stable imaging not only depends on the camera and lens, but also the matching of the appropriate light source and image processing algorithm cannot be ignored. For example, for the detection of reflective parts, the use of polarized light source combined with image enhancement algorithm can significantly reduce reflection interference; for the identification of tiny scratches, it is better to choose a strip or oblique angle light source.

At the same time, some smart cameras have built-in preprocessing algorithms and exposure control logic, which can automatically respond to changes in on-site light, keep the image grayscale and contrast consistent, and reduce false detection and missed detection.

4. Power supply and IO control

In automated production lines, visual systems usually need to be linked with PLC, robots, light source controllers and other equipment, so the number, type and power supply stability of their IO interfaces are particularly important.

Choosing a camera that supports multiple IO channels and multiple trigger modes (such as edge trigger, software trigger, external signal trigger) can improve the flexibility of system integration. In addition, by connecting an external stabilized power supply or using a visual device that supports wide voltage input, the problem of system disconnection caused by voltage fluctuations can be effectively avoided.

The stability of the machine vision system is not only reflected in the equipment selection, but also in every link from environmental adaptation to interface layout, from light source control to algorithm tuning. Only by starting from the overall perspective and paying attention to details can we create a truly reliable and efficient industrial vision solution.

On the road of future intelligent manufacturing, a stable vision system will become a solid support for enterprises to improve yield, reduce costs, and achieve flexible production. Zhixiang Vision will also continue to deepen industry applications and provide customers with smarter and higher-performance vision equipment and system services.