Embedded Smart devices enable IOT applications easily
Embedded smart devices integrate off-the-shelf sensors and processing platforms to enable compact, lower-power devices that can be more easily networked in IoT applications
Traditionally, inspection has relied on a camera or sensor transmitting data back to a central processor for analysis. For new IoT applications that integrate numerous image and data sources — including hyperspectral and 3D sensors outputting data in various formats — this approach poses a bandwidth challenge.
To help solve the impending bandwidth crunch, designers are investigating smart devices that process data and make decisions at the edge of the inspection network. These devices can take the form of a smart frame grabber that integrates directly into an existing inspection network or a compact sensor and embedded processing board that bypass a traditional camera.
Smart devices receive and process data, make a decision, and then send the data to other devices and local or cloud-based processing. Local decision-making significantly reduces the amount of data required to be transmitted back to a central processor. This lowers bandwidth demands while also reserving centralized processing power for more complex analysis tasks. The compact devices also allow intelligence to be placed at various points within the network.
A smart frame grabber, for example, can be integrated directly into a quality inspection line to receive data from an existing camera and make a decision. Lower-bandwidth processed data, instead of raw video data, can then be shared with the rest of the system. The smart frame grabber also converts all sensors into GigE Vision devices, providing a uniform data set to use across the application. This means advanced inspection capabilities, such as hyperspectral imaging and 3D scanning, can be integrated into an existing inspection system.
The smart frame grabber approach provides an economical way to implement edge processing compared with upgrading expensive installed cameras and processing systems. This also provides a fast avenue toward adding preliminary AI functionality to an existing inspection line.
Classic computer vision analysis excels at finding defects or matching patterns once it is tuned with a known data set. In comparison, AI is trainable, and as it gains access to a wider data set it’s able to locate, identify, and segment a wider number of objects or faults. New AI algorithms can be added to a smart frame grabber to perform more sophisticated analysis, with the camera and AI-processed video stream transmitted from the device to existing machine vision software.
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