Photo-identification (photo-id) is one of the main non-invasive
capture-recapture methods utilised by marine researchers for monitoring
cetacean (dolphin, whale, and porpoise) populations. This method has
historically been performed manually 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.

本文提出使用计算机视觉模型和后处理技术来创建一个完全自动的 Photo-identification (photo-id) 识别系统，该系统可以根据所有可用信息提供最可能的匹配，无需数据预处理，用于监测海洋中哺乳动物种群。系统通过目录相似性比较，能够处理以前未编目的个体，并标记这些个体以进行进一步调查。该文研究表明，系统在多个现实生活中的 Photo-id 相册上的表现非常好。