Finding meteorites after they shoot across the night sky is notoriously difficult. Now astronomers have done it for the first time using drones and machine vision algorithms.
The Desert Fireball Network in Australia is a system of cameras that monitor the night sky looking for fireballs. The network ensures that several cameras view every region of the sky so that the trajectory of the incoming rock can be triangulated with reasonable accuracy.
Even then, the success rate is low. ”[This approach] suffered from a relatively low success rate of ~20%, since an entire fall zone could rarely be covered in one trip,” say Seamus Anderson and colleagues at the Space Science and Technology Centre at Curtin University in Western Australia.This team has accelerated the search using drones and powerful machine vision algorithms.
“These fall conditions were promising enough to warrant a fieldtrip to survey the entire fall zone with a drone,” say Anderson and co. “We used a DJI M300 drone with a Zenmuse P1 camera to survey the 5.1 km2 fall line at 1.8 mm/pixel with 20% overlap among images in each direction,” say the researchers. In these images, they expected the meteorite to have an apparent size of between 20 and 65 pixels.“The first three days we spent onsite consisted of surveying with a drone, and processing data with our machine learning algorithm,” say Anderson and co.
For example, the team say it turns out their machine vision algorithm was not trained to find meteorites but instead identified anomalies of any kind. “During the course of devising this strategy, we have encountered false positives such as tin cans, bottles, snakes, kangaroos, and piles of bones from multiple animals,” they say.