DLC networks from: Application of a novel deep learning based 3D videography workflow to bat flight data

Studying the detailed biomechanics of flying animals relies on producing accurate three-dimensional coordinates for key anatomical landmarks. Traditionally, this is achieved through manual digitization of animal videos, a labor-intensive task that grows more so with increasing frame rates and numbers of cameras. In this study, we present a workflow that combines deep learning-powered automatic digitization with intelligent filtering and correction of mislabeled points using 3D information. We tested our workflow using a particularly challenging scenario – bat flight. First, we documented bats flying steadily in a wind tunnel. We compared the results from manually digitizing bats with markers applied to anatomical landmarks against using our automatic workflow on the same bats without markers. In our second test case, we compared manual digitization against our automated workflow for bats exhibiting complex maneuvers in a large flight arena. We found that the variation between the 3D coordinates from our workflow and those from manual digitization was less than a millimeter larger than the variation between 3D coordinates resulting from two different human digitizers. The reduced reliance on manual digitization stemming from this work has the potential to significantly increase the scalability of studies into the detailed biomechanics of animal flight.