When faced with complex, irregular, and tiny appearance defect detection, the combination of machine vision + AI deep learning is becoming an important tool for the new generation of automated quality control. So how do you get this combination?

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.

In the field of high-end manufacturing and precision machining, the requirements for detection accuracy are becoming more and more stringent, and micron-level defects may have a fatal impact on product quality. More and more companies are beginning to seek to build high-precision machine vision inspection systems with the help of industrial cameras to achieve automated, standardized, and traceable quality control processes.

With the continuous development of intelligent manufacturing, machine vision is gradually becoming an indispensable "prosecutor" for automated production lines. From the initial image acquisition to the output of test results after data processing, the complete one-stop visual system architecture is the key behind the formal efficient and stable production line. So, how to build a high-performance, implementable one-stop machine vision system? Below we will explain it to you.