Applying deep learning to right whale photo identification

• Published in: Conservation Biology
• Main Authors: Bogucki, Robert, Cygan, Marek, Khan, Christin Brangwynne, Klimek, Maciej, Milczek, Jan Kanty, Mucha, Marcin
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2018
• Subjects: Nature and Landscape Conservation; Ecology; Ecology, Evolution, Behavior and Systematics; Eubalaena glacialis; North Atlantic
• Online Access: http://dx.doi.org/10.1111/cobi.13226; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fcobi.13226; https://onlinelibrary.wiley.com/doi/pdf/10.1111/cobi.13226; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/cobi.13226

Abstract Photo identification is an important tool for estimating abundance and monitoring population trends over time. However, manually matching photographs to known individuals is time‐consuming. Motivated by recent developments in image recognition, we hosted a data science challenge on the crowdsourcing platform Kaggle to automate the identification of endangered North Atlantic right whales ( Eubalaena glacialis ). The winning solution automatically identified individual whales with 87% accuracy with a series of convolutional neural networks to identify the region of interest on an image, rotate, crop, and create standardized photographs of uniform size and orientation and then identify the correct individual whale from these passport‐like photographs. Recent advances in deep learning coupled with this fully automated workflow have yielded impressive results and have the potential to revolutionize traditional methods for the collection of data on the abundance and distribution of wild populations. Presenting these results to a broad audience should further bridge the gap between the data science and conservation science communities.