Abstract：
Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for animals and time-intensive for researchers. In recent years, the integration of deep learning in research has offered new methodological perspectives through the automatisation of complex tasks. Harnessing object detection and recognition technologies is increasingly used by researchers to achieve identification on video footage. This study represents a preliminary exploration into the development of a non-invasive tool for face detection and individual identification of Japanese macaques (Macaca fuscata) through deep learning. The ultimate goal of this research is, using identification done on the dataset, to automatically generate a social network representation of the studied population. The current main results are promising: (i) the creation of a Japanese macaques\' face detector (Faster-RCNN model), reaching an accuracy of 82.2% and (ii) the creation of an individual recogniser for the Kōjima Island macaque population (YOLOv8n model), reaching an accuracy of 83%. We also created a Kōjima population social network by traditional methods, based on co-occurrences on videos. Thus, we provide a benchmark against which the automatically generated network will be assessed for reliability. These preliminary results are a testament to the potential of this approach to provide the scientific community with a tool for tracking individuals and social network studies in Japanese macaques.
摘要：
个体认同在生态学和行为学中起着举足轻重的作用,特别是作为理解复杂社会结构的工具。然而,传统的识别方法通常涉及侵入性的物理标签，并且可以证明对动物具有破坏性，对研究人员来说是耗时的。近年来,深度学习在研究中的整合通过复杂任务的自动化提供了新的方法论观点。利用物体检测和识别技术越来越多地被研究人员用来实现对视频镜头的识别。这项研究是对通过深度学习开发用于日本猕猴（Macacafuscata）的人脸检测和个体识别的非侵入性工具的初步探索。这项研究的最终目标是,使用对数据集进行的识别，自动生成所研究人群的社交网络表示。当前的主要结果是有希望的：（i）创建了日本猕猴的面部检测器（Faster-RCNN模型），达到82.2%的准确率，(ii)为高岛猕猴种群创建个人识别器(YOLOv8n模型)，准确率达到83%。我们还通过传统的方法创建了一个高岛人口社交网络，基于视频上的共同事件。因此，我们提供了一个基准，将根据该基准评估自动生成的网络的可靠性。这些初步结果证明了这种方法的潜力，可以为科学界提供跟踪日本猕猴个人和社会网络研究的工具。
