Photo: Black Sage Technologies Searching the Skies: Black Sage Technologies artificial-intelligence system spots flying objects and determines whether theyre a threat.

Is it a bird? A plane? Or is it a remotely operated quadrotor conducting surveillance or preparing to drop a deadly payload? Human observers wont have to guessor keep their eyes glued to computer monitorsnow that theres superhuman artificial intelligence capable of distinguishing drones from those other flying objects. Automated watchfulness, thanks to machine learning, has given police and other agencies tasked with maintaining security an important countermeasure to help them keep pace with swarms of new drones taking to the skies.

The security challenge has only grown over the past few years: Millions of people have bought consumer drones and sometimes flown them into offlimits areas where they pose a hazard to crowds on the ground or larger aircraft in the sky. Off-the-shelf drones have also become affordable and dangerous weapons for the Islamic State and other militant groups in war-torn regions such as Iraq and Syria.

The need to track and possibly take down these flying intruders has spawned an antidrone market projected to be worth close to US $2 billion by the mid-2020s. The lions share of that haul will likely go to companies that can best leverage the power of machine-learning AI based on neural networks.

But much of the antidrone industry still lags behind the rest of the tech sector in making effective use of machine learning AI, says David Romero, founder and managing partner of Black Sage Technologies, based in Boise, Idaho. With machine learning, 90 percent of the work is figuring out how to make it so simple so that the customer doesnt have to know how machine learning works, says Romero. Many companies do that well, but not in the defense community.

He and Ross Lam, his Black Sage cofounder, are poised to take advantage of this opening for the upstarts looking to take on the defense industrys giants. They initially collaborated on a project that trained machine-learning algorithms to automatically detect deer on highways based on radar and infrared camera data. Eventually, they realized that the same approach could help spot drones and other unidentified flying objects.

Since the self-funded startups launch in 2015, it has won multiple contracts from the United States governmentincluding for U.S. military forces deployed in Iraq and Afghanistanand from U.S. allies.

Romero says its fairly straightforward to apply machine learning to the task of automatically detecting and classifying flying objects. But because the stakes are highmistakenly shooting down a small passenger plane or failing to take out an explosives-laden drone intruder could be equally disastrousBlack Sage puts its system through a rigorous training phase when its installed at a new site. The systems radar and infrared cameras capture information about each unidentified flying objects velocity, size, altitude, and so forth. Then a human operator helps train the machine-learning algorithms by positively identifying certain classes of drones (rotor or fixed-wing) as well as other objects such as birds or manned aircraft. For proof that it has learned its lessons well, the AI is tested against 20 percent of the positively identified data setthe part reserved specifically for cross validation.

Another company called Dedroneoriginally based in Kassel, Germany, but currently headquartered in San Franciscois taking a similar approach. When a Dedrone system is being installed at a new site, humans label unfamiliar objects as part of the training process, which also updates the companys proprietary DroneDNA library. Since its launch in 2014, Dedrones machine-learning software has helped safeguard events and locations such as a Clinton-Trump presidential debate, the World Economic Forum, and CitiField, home of the New York Mets baseball team.

Each time we update DroneDNA, we process over 250 million different images of drones, aircraft, birds, and other objects, says Michael Dyballa, Dedrones director of engineering. In the past eight months, weve annotated 3 million drone images.

Though Black Sages and Dedrones automated detection systems are said to be capable of running without human assistance after their respective training phases, the companies clients may choose to put humans in the loop for engaging active defenses, such as jammers or lasers, to take down flying intruders. Such caution is critical at sites like airports, where drone detection accuracy greater than 90 percent still means the occasional false alarm or case of mistaken identity. Even so, a humans interpretive ability can only supplement the ceaseless vigilance that AI systems will need to provide as the number of drones continues to rise.

Link:

AI to Ensure Fewer UFOs - IEEE Spectrum