Deep Learning AI Improves Drone Search and Rescue Operations in Scottish Highlands

Thursday - May 30 2024, 21:30 UTC - 5 months ago

Rescue teams in the Scottish Highlands often use drones to aid in search and rescue operations, but with limited battery life and vast terrain, finding the right area to search is crucial. Jan-Hendrik Ewers and his team at the University of Glasgow have developed a deep learning algorithm to improve the efficiency of drone search patterns. This new system could potentially save lives by speeding up response times and locating missing persons in a fraction of the time.

If a hiker gets lost in the rugged Scottish Highlands, rescue teams sometimes send up a drone to search for clues of the individual’s route—trampled vegetation, dropped clothing, food wrappers. But with vast terrain to cover and limited battery life, picking the right area to search is critical. Traditionally, expert drone pilots use a combination of intuition and statistical search theory—a strategy with roots in World War II-era hunting of German submarines—to prioritize certain search locations over others .

Jan-Hendrik Ewers and a team from the University of Glasgow recently set out to see if a machine learning system could do better.Ewers grew up skiing and hiking in the Highlands, giving him a clear idea of the complicated challenges involved in rescue operations there. "There wasn't much to do growing up, other than spending time outdoors or sitting in front of my computer," he says. "I ended up doing a lot of both .

"To start, Ewers took datasets of search and rescue cases from around the world, which include details such as an individual’s age, whether they were hunting, horseback riding or hiking, and if they suffered from dementia, along with information about the location the person was eventually found—by water, buildings, open ground, trees, or roads. He trained an AI model with this data, in addition to geographical data from Scotland .

The model runs millions of simulations to reveal the routes a missing person would be most likely to take under their unique circumstances. The result is a probability distribution—a heat map of sorts—indicating the priority search areas.With this kind of probability map, the team showed that deep learning techniques could be used to design more efficient search paths for drones. In research published last week on arXiv, which has not yet been peer reviewed, the team tested its algorithm against two common search patterns: the "lawnmower," in which a drone would fly over a target area in a series of simple stripes, as well as an algorithm similar to Ewers’ but less adept at working with probability distribution maps .

In virtual testing, Ewers’ algorithm beat both of those approaches in two key measures; the distance a drone would have to fly to locate the missing person, and the percentage of time the person was found. While the lawnmower and existing algorithmic approach found the person 8% of the time and 12% of the time, respectively, Ewers’ approach found them 19% of the time. If it proves successful in real rescue situations, the new system could speed up response times, and save more lives, in scenarios where every minute counts .

"The search and rescue domain in Scotland is extremely varied, and also quite dangerous," Ewers says. Emergencies can arise in thick forests on the Isle of Arran, the steep mountains and slopes around the Cairngorm Plateau, or the faces of Ben Nevis, one of the most revered but dangerous rock climbing destinations in Scotland. "Being able to send up a drone and efficiently search with it could potentially save lives .

"Search and rescue experts say that using deep learning to design more efficient drone routes could help locate missing persons in a fraction of the time.