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 offered new methodological
perspectives through automatization 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 identifications 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 a 82.2%
accuracy and (ii) the creation of an individual recognizer for K{\=o}jima
island macaques population (YOLOv8n model), reaching a 83% accuracy. We also
created a K{\=o}jima 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 innovative
approach to provide the scientific community with a tool for tracking
individuals and social network studies in Japanese macaques.

通过深度学习，本研究初步探索了一种非侵入性工具的开发，用于检测和识别日本猕猴的个体身份，旨在自动生成对研究种群进行社会网络分析的社交网络图。在研究中，建立了日本猕猴面部检测器（Faster-RCNN 模型），准确率达到 82.2％，并建立了针对小岛日本猕猴种群的个体识别器（YOLOv8n 模型），准确率达到 83％。此外，通过传统方法，基于视频中的共现性，还创建了小岛种群的社会网络图，从而为自动生成网络的可靠性提供基准。这些初步结果证明了这种创新方法在追踪日本猕猴个体和社会网络研究方面的潜力，为科学界提供了一个工具。

深度学习对 日本猕猴 中的自动检测和面部识别的应用：阐明社会网络

Deep Learning for Automatic Detection and Facial Recognition in Japanese  Macaques: Illuminating Social Networks

The latest advancements in artificial intelligence technology have opened
doors to the analysis of intricate behaviours. In light of this, ethologists
are actively exploring the potential of these innovations to streamline the
time-intensive process of behavioural analysis using video data. In the realm
of primatology, several tools have been developed for this purpose.
Nonetheless, each of these tools grapples with technical constraints that we
aim to surmount. To address these limitations, we have established a
comprehensive protocol designed to harness the capabilities of a cutting-edge
tool, LabGym. Our primary objective was to evaluate LabGym's suitability for
the analysis of primate behaviour, with a focus on Japanese macaques as our
model subjects. We have successfully developed a model that demonstrates a high
degree of accuracy in detecting Japanese macaques stone-handling behaviour. Our
behavioural analysis model was completed as per our initial expectations and
LabGym succeed to recognise stone-handling behaviour on videos. However, it is
important to note that our study's ability to draw definitive conclusions
regarding the quality of the behavioural analysis is hampered by the absence of
quantitative data within the specified timeframe. Nevertheless, our model
represents the pioneering endeavour, as far as our knowledge extends, in
leveraging LabGym for the analysis of primate behaviours. It lays the
groundwork for potential future research in this promising field.

利用最新的人工智能技术进行行为分析方面的研究是一项具有潜力的领域，该论文以 LabGym 作为切入点，成功地开发了一个模型，具备高度准确性，用于检测日本猕猴的拿石行为。