부산대학교 도서관

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 Galapagos 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 Galapagos 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%. Keywords: artificial intelligence (AI) platform; deep learning; Galapagos Islands; image classification; machine learning; Pseudalsophis; racer snake; region-based convolutional neural network (R-CNN); snake species
Simple Summary: The snakes in Galapagos are the least studied group of vertebrates in the archipelago. The conservation status of only four out of nine recognized species has been formally [...]