Automated conservation with deep learning AI for camera-trap identification of species and individuals (Ref: 4659)

该项目将使用保护生态学中的深度学习方法，从相机陷阱图像中自动识别物种或个体。该主题有望扩大保护倡议，并解决保护研究的一个关键瓶颈：野生动物的识别，这是一个资源和耗时的问题。它还将拓宽应用数学和计算机科学领域，以进一步研究该学科在解决现代世界问题（如有充分记录的生物多样性丧失危机）方面的能力。该项目将从三个层面开始关注黑猩猩的识别。首先，使用黑猩猩相机陷阱图像，我们将开发软件，使用深度学习人工智能识别图像中的物种。我们将使用的编码平台是PyTorch，该平台先前已用于其他物种的此类AI训练（Ramirez, 2022; Kholiavchenko, 2022; Lamba & Cassey, 2019; Wearn, Freeman & Jacoby, 2019）。这项工作将利用和发展机器学习方法，特别是深度神经网络。其次，在物种水平上识别个体的软件将被创建，例如使用面部识别和考虑黑猩猩移动耳朵的能力。每只黑猩猩都将获得一个唯一的识别号码。第三，该软件将被训练来识别视觉疾病的症状，比如麻风病，这是迄今为止还没有尝试过的。进一步的扩展也是可能的，比如在相机陷阱图像中识别豹子个体的指纹识别。管理人员与南非一个保护区的现有联系可能会在他们的豹子保护计划中产生有趣的应用。利用摄像机捕捉到的图像，在物种层面上自动识别豹子和豹子个体，这将促进公园内豹子的保护。该软件的开发也将在PyTorch上进行，使用“平面”的打印补丁来区分个体。在算法经过充分训练后（例如，与人类志愿者的准确率达到99.6%,Sreedevi, 2022），可以探索更广泛的适用性，包括人口预测矩阵（PPM）模型和信息价值理论（与昆士兰大学合作），以揭示更多信息：-以最低的成本/相机部署率，适当捕捉种群的最佳相机陷阱数量（和使用的时间），即相机陷阱在什么时候只记录相同的个体？-物种在保护区的分布（包括阶段结构分布）。-在不使用GPS项圈等侵入性技术的情况下，追踪物种的个体，通过记录它们经过相机陷阱的动作。这项开创性的研究与现实世界的应用有着极其紧密的联系，这些应用远远超出了这个项目的范围。通过相机陷阱数据集将深度学习人工智能添加到识别中，不仅有望减少人工完成此类任务的时间，还有助于消除保护研究中的巨大瓶颈。因此，利用计算机科学的力量来帮助保护濒危物种。

The project will use deep learning approaches in conservation ecology for the automated identification of species or individuals from camera trap images. The topic promising to scale up conservation initiatives and to deal with a crucial bottleneck of conservation research: identification of wild animals which is resource- and time-consuming. It will also broaden the field of applied mathematics and computer science to further research into the discipline's power to solve modern world issues such as the well-documented biodiversity loss crisis. The project will begin with a focus on chimpanzee identification at three levels. First, using chimpanzee camera trap images, we will develop software to recognise the species within images using deep learning AI. The coding platform we will use is PyTorch, which has been previously used for such AI training for other species (Ramirez, 2022; Kholiavchenko, 2022; Lamba & Cassey, 2019; Wearn, Freeman & Jacoby, 2019). This work will draw on and develop machine learning approaches, specifically deep neural networks. Second, software to identify individuals at a species-level will be created, for example using facial recognition and accounting for the chimpanzees' ability to move their ears. Each chimpanzee will receive a unique identification number. Third, the software will be trained to recognise symptoms of visual diseases, such as leprosy, which has since not been attempted. Further extensions are also possible, such as print-recognition to identify leopard individuals in camera trap images. Existing contacts to a reserve in South Africa by the supervisors could lead to interesting appplications in their leopard conservation programme. Automating the identification of leopards and individuals at a species-level in readily available camera-trap images would propel their conservation within the park. The development of this software will also be on PyTorch, using 'flat' patches of print to distinguish between individuals.After the algorithms have been adequately trained (for example to match human volunteer accuracy at 99.6%, Sreedevi, 2022), wider applicability includin Population Projection Matrix (PPM) modelling and Value of Information theory (in collaboration with the University of Queensland) can then be explored to reveal more information on: - The optimal number of camera-traps (and time used) to adequately capture the population at a minimum cost/camera deployment rate, i.e. at what time are camera-traps simply recording the same individuals?- The distributions of species across reserves (including stage-structured distributions).- Tracking individuals of a species without the use of invasive techniques such as GPS collars by their recorded movements past the camera traps.This pioneering research has extremely strong links to real-world applications that extend further than the scope of this project. The addition of deep learning AI to identifications through camera-trap datasets not only promises to decrease the time of human-effort on such a task, it facilitates the draining of a vast bottleneck in conservation research. Thus, harnessing the power of computer science to aid in the conservation of threatened species.