Machine Vision Makes the Move to IoT
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. These advancements have been, in large part, attributed to two factors: the expansion of networking capabilities and the availability of lower-cost devices. In the vision market, by comparison, these same factors have been key challenges that have instead slowed the adoption of IoT.
The increasing number of imaging and data devices within an inspection system, combined with more advanced sensor capabilities, poses a growing bandwidth crunch in the vision market. Cost is also a significant barrier for the adoption of IoT, particularly when considering an evolutionary path toward incorporating machine learning in inspection systems
Evolving toward IoT and AI
IoT promises to bring new cost and process benefits to machine vision and to pave the path toward integrating artificial intelligence (AI) and machine learning into inspection systems.
The proliferation of consumer IoT devices has been significantly aided by the availability of lightweight communication protocols (Bluetooth, MQTT, and Zigbee, to name just a few) to share low-bandwidth messaging or beacon data. These protocols provide “good enough” connectivity in applications when delays are acceptable or unnoticeable. For example, you likely wouldn’t notice if your air conditioner took a few seconds to start when you got home.
In comparison, imaging relies on low-latency, uncompressed data to make a real-time decision. Poor data quality or delivery can translate into costly production halts or secondary inspections — or worse, a product recall that does irreparable brand harm.
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