Cameras are not what they used to be. With the advances in IoT technologies and app development, it is now possible to make cameras IoT-ready in only a few simple steps. And vision AI is especially relevant here. It applies ML models to captured visual data in order to identify patterns, detect and classify objects, or segment images. In this sense, vision AI not simply replicates but also enhances human sensory capacity. Read on to see where to utilize vision AI in industrial IoT.
In the world of IoT, cameras are just another edge device that streams volumes of valuable data and provides insight into ongoing operations. Industrial-grade cameras are usually equipped with an internet connection and processors allowing them to perform smart operations right on the device and transmit the initial data for additional insight generation.
IoT cameras, then, are not just about security and surveillance. On top of that, they offer unparalleled insight into operations, track efficiencies, and communicate with larger IoT systems to make sure that everything is running as planned.
Vision AI Applications
So what are the main areas where you can use vision AI? In short, that would be any scenario that allows you to capture and process visual objects in video or static images. The technology can be utilized in industrial manufacturing, automotive, retail, logistics, or healthcare. Below are some typical applications:
- Edge detection. One of the oldest techniques in computer vision, edge detection is when an algorithm is tasked with detecting the boundaries of objects within a static or a moving image.
- Object detection. Typically, here an ML model is trained to identify specific objects within a video or a static image.
- Image classification. Here algorithms apply relational content and contextual data to objects within images in order to determine what they are, i.e. to what class of objects they belong.
- Image segmentation. Here an image is partitioned into regions based on pixel characteristics. For instance, you can train an algorithm to separate foreground from background and identify and differentiate between textures, shapes, or colors.
- Pattern recognition. In this case, an ML model is trained to detect sets of repeated characteristics or recurring sequences.
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Making Cameras Smart
With Record Evolution, you can connect cameras to the IoT platform just like any other embedded device, download apps from the IoT app store, install them within minutes, and run them instantly to make your cameras smart.
Further, you perform updates on the camera software over the air and tap into the various platform possibilities. These can vary from visualizations to ML model optimization or integration with AR solutions. Apps provide cameras with a wealth of capabilities that give manufacturing operations the necessary edge in the digital universe.
You connect cameras to your IoT platform, install and run apps on them, and visualize the findings in custom dashboards.
Beyond manufacturing, AI-powered IoT cameras are already in use in a diverse set of industries spanning retail, logistics, transportation, event management, and agriculture. Starting with just a few basic capabilities, you can expand on your existing IoT camera functionality by building and adding even more IoT apps on top of the existing solution.
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And, needless to say, cameras connected to the IoT platform adhere to the latest cyber security and privacy standards. Custom IoT apps make sure that only the data that is needed gets transferred to the cloud. Further, there is a clear dividing line between cloud data and data preprocessing on the edge device. This way, your IoT camera solution ensures GDPR compliance at all times and effectively protects any personally identifiable information from the outset.
Smart Camera Uses: An Example
What are the uses of smart IoT cameras? Cameras can intervene in scenarios where human perception is insufficient in identifying hazards. For example, smart cameras can detect a fire before humans can recognize it. This way, IoT cameras go beyond classic firefighting measures that rely on surveillance and simple sensor technology for smoke detection.
How? Approaches that depend on monitoring (by humans) can be flawed because of too much reliance on the human eye. The human eye, however, can only detect smoke once it has begun to spread and has technically become visible. As a consequence, there is a significant delay in responding to the fire hazard. AI-powered cameras installed at key locations on the shop floor can make all the difference here. And they can not only detect the fire hazard early on but will also transmit automated alerts.
This is just one example of the many possibilities that AI-powered IoT cameras hold. For detailed information, visit our REVIS page to learn about our smart camera system.
About Record Evolution
We are a data science and IoT team based in Frankfurt, Germany, that helps companies of all sizes innovate at scale. That’s why we’ve developed an easy-to-use industrial IoT platform that enables fast development cycles and allows everyone to benefit from the possibilities of IoT and AI.