Classification of Wildlife Based on Transfer Learning
Xihao Wang, Peihan Li, Chengxi Zhu · 2020
Wildlife is an important biological resource in China. Classifying images of wildlife through computer technology can help people identify wildlife, which is of great significance to help people understand and protect wildlife. Therefore, this issue is worth studying. Traditional methods mostly use standard Convolutional Neural Networks (CNN) to classify wild animal images, but these methods have disadvantages such as slow computing speed, long time consumption and low accuracy. With an attempt to address such issues, this paper proposes a method based on transfer-learning for classifying wild animal images. By using the pre-trained model it can save a lot of training time. The experimental results on Oregon Wildlife, using a public wildlife data set, show that the method proposed in this paper achieved 99.01% accuracy and is 57.82% more accurate than the standard Convolutional Neural Networks (CNN) method. Moreover, in terms of running time, the method presented in this paper has achieved higher efficiency, and the training time is 50% shorter than the standard method, which proves the superiority of the method proposed in this paper.