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。

面向自动鲸类照片识别：海洋生态中细粒度、少样本学习的框架

Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology

Methods for cetacean research include photo-identification (photo-id) and
passive acoustic monitoring (PAM) which generate thousands of images per
expedition that are currently hand categorised by researchers into the
individual dolphins sighted. With the vast amount of data obtained it is
crucially important to develop a system that is able to categorise this
quickly. The Northumberland Dolphin Dataset (NDD) is an on-going novel dataset
project made up of above and below water images of, and spectrograms of
whistles from, white-beaked dolphins. These are produced by photo-id and PAM
data collection methods applied off the coast of Northumberland, UK. This
dataset will aid in building cetacean identification models, reducing the
number of human-hours required to categorise images. Example use cases and
areas identified for speed up are examined.

本文介绍了通过照片识别和被动声学监测等方法来进行海豚研究，并制作出了北伯兰海豚数据集，以减少人工分类图像所需的时间和劳力，并且可以帮助建立鲸类识别模型。