Software techniques help smart device for cloud-base
Software techniques enable the design of virtual GigE Vision sensors that can be networked to share data with other devices and local or cloud-based processing.
Embedded smart devices enable more sophisticated processing at the sensor level. Key to this has been the introduction of lower-cost, compact embedded boards with processing power required for real-time image analysis. Embedded smart devices are ideal for repeated and automated robotic processes, such as edge detection in a pick-and-place system.
Like the smart frame grabber, embedded smart devices offer a straightforward path to integrating AI into vision applications. With local and cloud-based processing and the ability to share data between multiple smart devices, AI techniques can help train the computer model to identify objects, defects, and flaws while supporting a migration toward self-learning robotics systems.
Solving the connectivity challenge
The introduction of the GigE Vision standard in 2006 was a game changer that brought new levels of product interoperability and networking connectivity for machine vision system designers. Today’s designers contemplating IoT and AI face similar challenges, while also dealing with an increasing number of imaging and nonimaging sensors and new uses for data beyond traditional inspection. Once again, GigE Vision is providing the path forward to more sophisticated analysis. Industrial IoT promises the ability to leverage various types of sensors and edge processing to increase inspection speed and quality. However, 3D, hyperspectral, and infrared (IR) capabilities that would power high-definition inspection each have their own interface and data format. This means designers can’t easily create a mix-and-match inspection system to take advantage of advanced sensor capabilities or device-to-device networking. High-bandwidth sensors and an increased number of data sources also pose a bandwidth challenge.
Novel software techniques that convert any imaging or data sensor into a GigE Vision device ensure that each sensor speaks a common language. This enables seamless device-to- device communication across the network and back to local or cloud processing. Edge processing also significantly reduces the amount of data that needs to be transported, making wireless transmission for real-time vision applications a reality.
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