In the world of IoT, a designer can innovate through complementary technologies. In this article by Andreas Burghart, Principal IoT Technology Manager at Digi International, you can explore how medical and industrial IoT (IIoT) is pushing to the next level of innovative advancement.
How Machine Learning (ML) and Machine Vision (MV) are Changing Our World
Machine learning techniques can be applied to a wide range of machine vision image processing or classification tasks. And, thanks to the scale at which these applications are being adopted, it’s rapidly becoming cost-effective to add ML and MV capabilities to an increasing variety of IoT products.
Machine vision systems typically use digital sensors inside of industrial camera modules where images are processed and analyzed. When used with machine learning, machine vision applications can perform complex tasks reliably and consistently.
In healthcare applications, they can monitor patients or detect anomalies in radiological images, improving patient care or even saving lives.
In an industrial environment, they can provide quality control inspections of many identical items on a conveyor belt without error or fatigue. In fact, machine vision can deliver cost savings and quality control benefits in almost any mass production process that requires visual monitoring.
Business Benefits of Machine Learning Combined with Machine Vision
While there are many benefits of the combined power of machine learning and vision, some of the most important are those that drive real-world operational improvements and impact the bottom line.
- • Higher product quality: Inspection, measurement, gauging, and assembly verification.
- • Increased productivity: Routine, repetitive tasks can be performed fast and automatically, freeing staff for higher value activities.
- • Lower costs: Adding MV capabilities to equipment can improve performance and extend service life. MV systems in a factory setting also take up less room than human operators and don’t require the same level of safety infrastructure.
Why Compute at the Edge?
ML/MV applications function in real time. With edge computing, applications can process closer to where the business production is actually taking place. Edge computing is very advantageous in situations where bandwidth or latency are a constraint, such as in a manufacturing facility where dozens of items per minute are inspected by an ML/MV system as they roll by on a conveyor belt.
Performance Benefits of Edge Computing
Today’s compute effort is moving to the edge for massive improvements in efficiency and performance, including the following:
- • Reduced latency: Edge computing provides faster response times without the need for round-trip data-to-server usage.
- • Ability to operate offline: In case of a communications disruption, the system can collect, store and process data on its own.
- • Reduced cost: Networking costs are lowered as the flow of data is reduced.
- • Reduced power consumption: Battery-powered IoT devices can benefit with power savings since less data is sent.
Tools and Software Support Successful Implementations
Creating a machine learning solution is no small feat. The right hardware, development tools, and software components for machine learning-based image processing at the edge can go a long way toward a successful implementation.
Au-Zone Technologies offers a toolkit for the development of machine learning-based imaging systems on performance constraint embedded devices. The company’s inference engines supports MCU, CPU, and GPU cores with one standard workflow and model format, providing flexibility and portability across platforms. The Digit ConnectCore® 8X system on module (SOM) contains MCU, CPU, and GPU processing cores, allowing the inference engine to efficiently run on any of these compute resources.
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