Data from: A convolutional neural network for detecting sea turtles in drone imagery

1. Marine megafauna are difficult to observe and count because many
species travel widely and spend large amounts of time submerged. As such,
management programs seeking to conserve these species are often hampered
by limited information about population levels. 2. Unoccupied aircraft
systems (UAS, aka drones) provide a potentially useful technique for
assessing marine animal populations, but a central challenge lies in
analyzing the vast amounts of data generated in the images or video
acquired during each flight. Neural networks are emerging as a powerful
tool for automating object detection across data domains and can be
applied to UAS imagery to generate new population-level insights. To
explore the utility of these emerging technologies in a challenging field
setting, we used neural networks to enumerate olive ridley turtles
(Lepidochelys olivacea) in drone images acquired during a mass-nesting
event on the coast of Ostional, Costa Rica. 3. Results revealed
substantial promise for this approach; specifically, our model detected 8%
more turtles than manual counts while effectively reducing the manual
validation burden from 2,971,554 to 44,822 image windows. Our detection
pipeline was trained on a relatively small set of turtle examples (N=944),
implying that this method can be easily bootstrapped for other
applications, and is practical with real-world UAS datasets. 4. Our
findings highlight the feasibility of combining UAS and neural networks to
estimate population levels of diverse marine animals and suggest that the
automation inherent in these techniques will soon permit monitoring over
spatial and temporal scales that would previously have been impractical.