One way of thinking about an Edge AI device is as a magic lantern that lights up whenever it is triggered by a specific event. When an AI video security system identifies an intruder entering the building, for example, it could potentially set off lights flashing, alarm bells ringing, and urgent messages flying to make sure that the individual is apprehended before he does any serious damage.
The same principle applies to Edge AI devices for industrial, transportation, and other vertical applications. No matter whether it’s a defective product on a manufacturing line or an errant driver surreptitiously checking his smartphone for the latest sports news, the magic lantern will shine a bright light on it and issue a call for action.
One of the biggest challenges with deploying your magic lantern is not so much figuring out what data it needs to capture but in deciding which specific items or events it needs to highlight. If the device is flashing most of the time, it will very quickly become ambient lighting.
This is one reason why customization is so critical for successful Edge AI device deployments. No matter how clearly the initial use case has been defined, the device will need to be optimized once it has been installed to ensure that it captures the right data in the most efficient and effective manner. Something as seemingly simple as a poor camera placement can result in poor quality data and worse if it’s not swiftly addressed.
As the device collects and processes more data, its algorithms will also require constant tweaking to enhance the device’s functionality and performance by, for example, enabling it to see more clearly in murky light and dusty environments.
Once you get started, the possibilities for making your magic lanterns shine even brighter. Make sure that they have enough headroom to cast the sharpest possible light on the darkest corners of your operations.