[MARMAM] new publication: Applying deep learning to right whale photo identification

Christin Khan - NOAA Federal christin.khan at noaa.gov
Thu Dec 6 12:32:27 PST 2018

Bogucki, R. , Cygan, M. , Khan, C. B., Klimek, M. , Milczek, J. K. and
Mucha, M. (2018), Applying deep learning to right whale photo
identification. Conservation Biology. . DOI: 10.1111/cobi.13226


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 rightwhales (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 deeplearning 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.

*Keywords: *algorithm, automated image recognition, computer vision,
convolutional neural networks, Kaggle competition, machine learning, photo

- Christin

*Christin B. KhanFishery Biologist, Right Whale AerialsNOAA Northeast
Fisheries Science Centerchristin.khan at noaa.gov <christin.khan at noaa.gov> *

*www.cbkhan.com <http://www.cbkhan.com>617.256.4452*

*psb websitewww.nefsc.noaa.gov/psb/ <http://www.nefsc.noaa.gov/psb/>noaa
<https://www.facebook.com/NOAAFisheries>personal websitewww.cbkhan.com
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