Key Points:
- Event-based vision is a new approach to machine vision that captures and processes visual information more efficiently than frame-based vision.
- Event-based vision uses independent receptors, similar to the human eye and brain, to collect only the essential information, resulting in 10-1000x less data.
Machine vision systems used in industrial applications such as high-speed inspection cameras and robotic systems have typically relied on frame-based vision. However, this method has limitations in terms of capturing data in dynamic scenes, working in challenging lighting conditions, and operating with limited compute and power resources.
Event-based vision, on the other hand, offers a new approach that overcomes these limitations. Instead of recording entire frames at predefined intervals, event-based vision uses independent receptors to collect only the essential information, resulting in significantly less data. This approach enables better performance in dynamic scenes, capturing fast-moving subjects, and operating in low-light conditions.
Event cameras, which leverage neuromorphic techniques, have gained traction in machine vision applications in industrial automation, robotics, and automotive industries. These cameras offer benefits such as blur-free images, high-speed data capture, high resolution, high dynamic range, and shutter-free operation.
There are several common use cases for event-based vision in industrial applications. These include object tracking, fluid monitoring, vibration monitoring, and particle and object size monitoring. Event-based vision can be used to track objects with low compute power, monitor fluid dynamics in real time, track vibration patterns, and measure the size of objects moving at high speeds.
Prophesee, a company that developed event-based vision technology, offers development tools, algorithms, and open-source resources to facilitate the integration of event-based vision into machine vision systems.
In conclusion, event-based vision is a promising technology that enables more efficient and effective machine vision applications in industrial automation. Its ability to capture and process visual information in a more targeted and intelligent manner can improve performance, reduce data requirements, and overcome the limitations of traditional frame-based vision.