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How is Artificial Intelligence influencing the drone industry?

 Drones have evolved at such a rapid rate over the years it is hard to imagine life without it. So much so, now, the inclusion of artificial intelligence within the drone industry is due to change life as we know it.

The incorporation of AI (Artificial Intelligence) allows drone vendors to use data from sensors attached to the Drone in a bid to collect visual data.

So how has AI influenced the drone industry?

Until recently drones were only able to capture what the in-built camera saw. But now thanks to AI software, drones can soak up more of its surroundings, enabling it to track objects, map areas and provide analytical feedback in real time.

Neurala, based in Boston, is a network that helps drones sift through crowds to identify a person of interest. The company claims that with the use of AI software, it can understand an image of an individual in 20 minutes rather than hours or days, when carried out by traditional equipment. Using this service, is a company called the Lindbergh Foundation who use Neurala-powered drones to catch elephant poachers in Africa. By using the company’s image recognition, it can now monitor elephant herds and spot poachers miles before they reach the elephants.

Another company using AI powered drones is Skycatch that builds software that captures and analyses data from aerial images. Skycatche’s software turns images into 3D meshes or thermal images to get an overview of the land being surveyed. Japanese construction company, Komatsu, uses Skycatche’s drones on more than 5,000 sites. It now only takes 30 minutes to process aerial images compared to days for humans to derive the same task.

So how AI powered drones work?

There are two main components for this to be achieved. One of which is Computer Vision and the other is sensors. So lets start to explain exactly what Computer Vision is about.

According to reports, Computer Vision is gained through onboard image processing with a neural network. A neural network is a method used to install algorithms in machine learning. This enables a drone to carry out object detection, classification and tracking. So for example if a drone is used for deliveries, it can avoid collision, locate and track targets.

But to install neural network in drones researchers need to train the machine learning algorithms to recognise and categorise objects in a wide variety of contexts. This is created by marked images into the algorithms. This allows the neural network to differentiate one object from another.

And now we move on to Sensors. Sensors collect data including visual, positioning and environmental.

This information is fed to machine learning modules to gain knowledge on how a drone should respond to environmental conditions, where it is allowed to fly to and avoidable objects.

This data is then fed to machine learning models to determine how a drone should respond to environmental conditions, what objects it should prioritize or avoid, and where it can fly to. Often sensor data is also used after a drone has landed in non-flight related analyses. For example, to find potential mining locations or to evaluate the water quality of reservoirs.

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