Machine vision applications are becoming increasingly widespread, not only in industry but also in everyday life, such as in traffic monitoring and the cameras used during car journeys, which allow people to see a panoramic view of the car's exterior. The emergence of machine vision can be considered an important leap in automation. Today, we will introduce the advantages of embedded machine vision system lighting.
1. Machine vision has long been used in industrial automation systems to improve production quality and output by replacing traditional manual inspection. From picking and placing, object tracking, to measurement and defect detection, the use of visual data collected by illuminator machine and other machines can improve the performance of the entire machine vision system by providing simple failure information or closed-loop control circuits.
2. The greatest technological progress in the field of machine vision may have been in processing power. With processor performance doubling every two years and continued attention to parallel processing technologies such as multi-core CPUs, GPUs, and FPGAs, machine vision lighting designers can now apply highly complex algorithms to visual data and create smarter systems. Thereby improving the accuracy of machine vision measurement and detection.
The development of processing technology brings new opportunities, not just for more intelligent or powerful algorithms. Let's take a look at application cases for adding machine vision functionality to manufacturing machines. These systems are traditionally designed as intelligent subsystem networks that form collaborative distributed control systems. This system allows for modular design, but adopting this hardware-centric approach may lead to performance bottlenecks.
However, as system performance improves, adopting this hardware-centric approach may become challenging because these machine vision systems typically use a mixture of time-critical and non-time-critical protocols to connect. Connecting these different systems through various communication protocols can lead to bottlenecks in terms of delay, determinism, and throughput.
For example, if a designer tries to develop an application using this distributed architecture and must maintain close integration between the vision and motion systems, as required in a machine vision servo, they may face significant performance challenges due to a lack of processing power. In addition, because each subsystem has its own controller, this actually reduces processing efficiency.
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