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Smart helpers with image recognition

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Drones can eff ectively complement the work of rescue services teams in forests and mountain areas

2016-05-04

Every year, thousands of people lose their way in forests and mountain areas. In Switzerland alone, emergency centers respond to around 1,000 calls annually from injured and lost hikers. But drones can effectively complement the work of rescue services teams.

 - The newly developed software is based on an adaptive network.
© UZH; USI; SUPSI
The newly developed software is based on an adaptive network.

Because they are inexpensive and can be rapidly deployed in large numbers, they reduce the response time and the risk of injury to missing persons and rescue teams alike. A group of Swiss researchers has developed artificial intelligence software to teach a small quadrocopter to autonomously recognize and follow forest trails. A premiere in the fields of artificial intelligence and robotics, this success means drones could soon be used in parallel with rescue teams to accelerate the search for people lost in the wild. “While drones flying at high altitudes are already being used commercially, drones cannot yet fly autonomously in complex environments, such as dense forests.

In these environments, any little error may result in a crash, and robots need a powerful brain in order to make sense of the complex world around them,” says Prof. Davide Scaramuzza from the University of Zurich. The drone used by the Swiss researchers observes the environment through a pair of small cameras, similar to those used in smartphones. Instead of relying on sophisticated sensors, their drone uses very powerful artificial- intelligence algorithms to interpret the images to recognize man-made trails. If a trail is visible, the software steers the drone in the corresponding direction.

In order to gather enough data to “train” their algorithms, the team hiked several hours along different trails in the Swiss Alps and took more than 20,000 images of trails using cameras attached to a helmet. The effort paid off: When tested on a new, previously unseen trail, the deep neural network was able to find the correct direction in 85 per cent of cases; in comparison, humans faced with the same task guessed correctly 82 per cent of the time.