Towards automatic cetacean photo-identification: A framework for fine-grain, few-shot learning in marine ecology

Cameron Trotter, Nick Wright, Stephen McGough, Matt Sharpe, Barbara Cheney, Monica Arso Civil, Reny Tyson Moore, Jason Allen, Per Berggren

Abstract:

Photo-identification (photo-id) is one of the main
non-invasive capture-recapture methods utilised by marine re-
searchers for monitoring cetacean (dolphin, whale, and porpoise)
populations. This method has historically been performed manu-
ally resulting in high workload and cost due to the vast number of
images collected. Recently automated aids have been developed to
help speed-up photo-id, although they are often disjoint in their
processing and do not utilise all available identifying information.
Work presented in this paper aims to create a fully automatic
photo-id aid capable of providing most likely matches based
on all available information without the need for data pre-
processing such as cropping. This is achieved through a pipeline
of computer vision models and post-processing techniques aimed
at detecting cetaceans in unedited field imagery before passing
them downstream for individual level catalogue matching. The
system is capable of handling previously uncatalogued individuals
and flagging these for investigation thanks to catalogue similarity
comparison. We evaluate the system against multiple real-life
photo-id catalogues, achieving mAP@IOU[0.5] = 0.91, 0.96 for
the task of dorsal fin detection on catalogues from Tanzania and
the UK respectively and 83.1, 97.5% top-10 accuracy for the task
of individual classification on catalogues from the UK and USA.