Using pose estimation to identify regions and points on natural history specimens

Yichen He, Christopher R. Cooney, Steve Maddock, Gavin H. Thomas

Abstract: Author summary As the digitisation of natural history collections continues apace, a wealth of information is waiting to be mobilised from these vast digital datasets that can help address many evolutionary and ecological questions. Deep Learning has achieved success on many real-world tasks such as face recognition and image classification. Here, we use deep learning to measure phenotypic traits of specimens by placing points on photos of birds and periwinkles. We show that the measurements produced by Deep Learning are generally accurate and very similar to manual measurements taken by experts. As Deep Learning methods vastly reduce the time required to produce these measurements, our results demonstrate the great potential of Deep Learning for future biodiversity studies.