Today, designers are converting the image feeds from these sensors into GigE Vision to use traditional machine vision processing for analysis. Looking ahead, there will be obvious value in fully integrating the output from all of the sensors within an application to provide a complete data set for analysis and eventually AI.
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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
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.
The introduction of the GigE Vision standard in 2006 brought new levels of product interoperability and networking connectivity for machine vision system designers, paving the way for the emergence of IoT.
One of the most hyped technologies in recent years has been the Internet of Things (IoT), a trend that has entered our consumer lives via home monitoring systems, wearable devices, connected cars, and remote health care.
Machine vision systems are a staple in production lines for barcode reading, quality control and inventory management. And, as the Industrial Internet of Things (IIoT) continues to expand its reach, these systems have become crucial data collectors.
These solutions often have long replacement cycles and are less prone to disruption. Due to the increasing demands for…