S&T Creates Better Drone Detection Through Machine Learning, Cameras


Available for logged-in reporters only

Newswise — Visual detection of drones has never been considered as effective as its thermal, radio or acoustic counterparts. The trouble is always discriminating between different moving objects in view. Typically, a bird or even a plastic bag caught in the wind might be mistaken for a drone, which is why most discrimination methods have primarily focused on heat and acoustic signatures in the past (though acoustic signatures also tend to become less useful in urban areas with higher levels of background noise).

Combined with machine learning, however, a camera can tell a different story. Today, this budding technology is helping the Department of Homeland Security (DHS) Science and Technology Directorate (ST) and Sandia National Laboratories create more precise drone detection capability through visuals alone.

“If you have a video of something, you can kind of identify it based on certain characteristics,” explained

... read more at: https://www.newswise.com/articles/st-creates-better-drone-detection-through-machine-learning%2C-cameras