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。这一主题承诺扩大保护行动，并解决保护研究的一个关键瓶颈：识别野生动物，这是耗费资源和时间的。它还将拓宽应用数学和计算机科学的领域，以进一步研究该学科解决现代世界问题的能力，如有据可查的生物多样性丧失危机。该项目将从三个层面开始，重点是黑猩猩的识别。首先，使用黑猩猩相机捕捉的图像，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，它以前曾用于其他物种的此类人工智能训练(Ramirez，2022；Kholiavchenko，2022；Lamba&Cassey，2019；Wain，Freeman&Jacoby，2019)。这项工作将借鉴和发展机器学习方法，特别是深度神经网络。其次，将创建在物种水平上识别个体的软件，例如使用面部识别和黑猩猩移动耳朵的能力。每只黑猩猩都会收到一个唯一的识别码。第三，该软件将接受培训，以识别麻风病等视觉疾病的症状，自那以来，该软件一直没有尝试过。进一步的扩展也是可能的，例如识别相机陷阱图像中的豹子个体的指纹识别。监管人员目前与南非的一个保护区有联系，可能会在他们的豹保护计划中产生有趣的应用。在容易获得的相机陷阱图像中，在物种层面上自动识别豹和个体将推动公园内对它们的保护。该软件的开发也将在PyTorch上进行，使用平面打印斑块来区分个体。在算法经过充分训练(例如，匹配人类志愿者99.6%的准确率，SreeDevi，2022)之后，可以探索更广泛的适用性，包括人口预测矩阵(PPM)建模和信息论的价值(与昆士兰大学合作)，以揭示关于以下方面的更多信息：-以最低成本/相机部署速度充分捕捉种群的最佳相机陷阱数量(和使用的时间)，也就是说，相机陷阱在什么时候简单地记录相同的个体？-物种在保护区的分布(包括阶段结构的分布)。-在不使用侵入性技术(如GPS项圈)的情况下，通过记录的经过相机陷阱的活动来跟踪物种的个体。这项开创性的研究与实际应用具有极强的联系，这些应用超出了本项目的范围。通过相机陷阱数据集将深度学习人工智能添加到身份识别中，不仅有望减少人类在此类任务上的时间，还有助于消除保护研究中的一个巨大瓶颈。因此，利用计算机科学的力量来帮助保护受威胁的物种。

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.