AI helps marine ecologists study deep-sea species

Led by the University of Plymouth, the research used images taken from an unmanned submarine equipped with a high-resolution camera. In May 2016, one of the UK’s national AUVs took more than 150,000 images from more than a kilometre below sea level during a single dive in the North Atlantic. The AUV travelled roughly 3m above the seabed at 2.2 knots, taking an image every second. Around 1,200 of these were manually analysed, identifying 110 species and 40,000 individual creatures.

Using the same set of images, the team used Google’s open-source Tensorflow AI software to teach a pre-trained Convolutional Neural Network (CNN) to identify individuals of various deep-sea species found in the AUV images. The CNN demonstrated accuracy of around 80 per cent success at identifying creatures correctly, but this rose as high as 93 per cent for specific species when enough data was available to train the algorithm.

Manual analysis can vary greatly in accuracy, ranging from 50-95 per cent, but is slow and laborious. It’s hoped that the Plymouth research, published in Marine Ecology Progress Series, can lay the ground for automated deep-sea analysis that will help shed light on the darkest recesses of the ocean.