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Human Eyes Assist Drones, Teach Machines to See


The researchers used Namibia's Kuzikus wildlife reserve to train and test their new system.

A new strategy combining crowdsourcing and machine learning appears able to rapidly interpret aerial images from drones.

Credit: Friedrich Fedor Reinhart

Researchers have developed a new strategy combining crowdsourcing and machine learning for rapidly interpreting aerial images captured by camera drones.

As a test of their approach, the researchers surveyed Namibia's Kuzikus wildlife reserve to count the resident animal population, and they used the www.micromappers.org crowdsourcing platform to upload the gathered drone images for manual analysis by volunteers.

The volunteers were tasked with clicking through a stack of images, identifying all animals, and outlining them on their screens. "Within two days, they had evaluated 98 percent of the 26,000 images that had been uploaded," says Swiss Federal Institute of Technology researcher Stephane Joost.

Fifty percent of these annotated images were then utilized to train an automatic object-recognition algorithm, which was then tested on the remaining pictures.

"The 500 digital volunteers did generate a number of false positives, tracing features that in actual fact were not animals," Joost observes. "Despite that, their analysis was certainly good enough to serve as training data for the computer algorithm."

Joost says the new approach could expedite image data analysis to aid disaster response operations.

From Ecole Polytechnique Federale de Lausanne
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