부산대학교 도서관

Wildlife plays a vital role in balancing the environment. It also provides stability to different natural processes of nature. In recent year, there are many animals which are facing the danger of extinction. The reason for animal extinction is natural occurrences such as climatic heating, cooling, or changes in sea levels. In literate, many techniques are proposed to detect and classify animals, but each technique has a limitation. In this paper, we propose a novel framework using deep convolutional neural networks (D-CNN) and k Nearest Neighbors (kNN) to detect animals. The dataset contains four class snow leopard, Marco polo sheep, Himalayan bear, and other animals. Many D-CNN like AlexNet, ResNet-50, VGG-19, and inception v3 are used to extract features. The experimental results verify that inception v3 integrated with kNN outperforms other D-CNNs. It also has more accuracy of 98.3% with a classification error of 2%, which is quite negligible.