Patel Anika, Cheung Lisa, Khatod Nandini, Matijosaitiene Irina, Arteaga Alejandro, Jr Joseph W Gilkey
Data Science Institute, Saint Peter's University, Jersey City, NJ 07306, USA.
Institute of Environmental Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania.
Animals (Basel). 2020 May 6;10(5):806. doi: 10.3390/ani10050806.
Real-time identification of wildlife is an upcoming and promising tool for the preservation of wildlife. In this research project, we aimed to use object detection and image classification for the racer snakes of the Galápagos Islands, Ecuador. The final target of this project was to build an artificial intelligence (AI) platform, in terms of a web or mobile application, which would serve as a real-time decision making and supporting mechanism for the visitors and park rangers of the Galápagos Islands, to correctly identify a snake species from the user's uploaded image. Using the deep learning and machine learning algorithms and libraries, we modified and successfully implemented four region-based convolutional neural network (R-CNN) architectures (models for image classification): Inception V2, ResNet, MobileNet, and VGG16. Inception V2, ResNet and VGG16 reached an overall accuracy of 75%.
野生动物的实时识别是一种新兴且有前景的野生动物保护工具。在这个研究项目中，我们旨在对厄瓜多尔加拉帕戈斯群岛的鞭蛇进行目标检测和图像分类。该项目的最终目标是构建一个人工智能（AI）平台，以网络或移动应用程序的形式，为加拉帕戈斯群岛的游客和公园护林员提供实时决策和支持机制，以便从用户上传的图像中正确识别蛇的种类。我们使用深度学习和机器学习算法及库，修改并成功实现了四种基于区域的卷积神经网络（R-CNN）架构（图像分类模型）：Inception V2、ResNet、MobileNet和VGG16。Inception V2、ResNet和VGG16的总体准确率达到了75%。
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