Shepley Andrew, Falzon Greg, Lawson Christopher, Meek Paul, Kwan Paul
School of Science and Technology, University of New England, Armidale, NSW 2350, Australia.
College of Science and Engineering, Flinders University, Adelaide, SA 5001, Australia.
Sensors (Basel). 2021 Apr 8;21(8):2611. doi: 10.3390/s21082611.
Image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources, particularly for camera trap images. Ease of training and use of transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy.
图像数据是全球生物多样性保护和管理中使用的主要生态数据来源之一。然而，对大量图像进行分类和解释既耗时又耗费资源，尤其是在相机陷阱监测的情况下。深度学习模型已被用于完成这项任务，但由于它们无法推广到新环境且性能不一致，往往不适用于特定应用。需要针对特定的物种群体和环境开发模型，但实现这一目标所需的技术技能是生态学家使用该技术的关键障碍。因此，迫切需要通过提供一个易于使用的软件应用程序，让非技术用户能够训练自定义目标检测器，从而使深度学习技术更易于获取。U-Infuse通过使生态学家能够使用公开可用的图像和/或他们自己的图像来训练定制模型，而无需特定的技术专长，解决了这个问题。自动标注和标注编辑功能最大限度地减少了手动标注和预处理大量图像的限制。U-Infuse是一个免费且开源的软件解决方案，支持多类和单类训练以及目标检测，使生态学家能够在自己的设备上，针对其应用进行定制，访问通常只有计算机科学家才能使用的深度学习技术，而无需共享知识产权或敏感数据。它为生态从业者提供了以下能力：（i）在用户友好的图形用户界面（GUI）中轻松实现目标检测，生成物种分布报告和其他有用的统计数据；（ii）使用公开可用的和自定义的训练数据自定义训练深度学习模型；（iii）对图像进行有监督的自动标注以进行进一步训练，并受益于编辑标注以确保高质量的数据集。生态从业者广泛采用U-Infuse将通过以最少的时间和资源支出处理大量图像数据，特别是相机陷阱图像，来改善生态图像分析和处理。易于训练和使用迁移学习意味着可以快速训练特定领域的模型，并经常更新，而无需计算机科学专业知识或数据共享，从而保护知识产权和隐私。
