Automatic detection for the world's rarest primates based on a tropical rainforest environment

Abstract

Hainan gibbons are the most endangered primates in the world and are at risk of extinction at any time. Infrared trigger camera technology is one of the most effective means at present, however, the data obtained are characterized by massive fragmentation, which is not conducive to post-processing and analysis. Therefore, a computer vision detection model for Hainan gibbons is proposed for the first time in this paper. Because of the foggy tropical rainforest, the image is pre-processed through an image filter whose parameters are obtained from a convolutional neural network. And then input it to the YOLO V3 network for Hainan gibbon detection. We produced a dataset containing 6000 images of Hainan gibbons for experimental training. The detection quality of Hainan gibbons based on YOLO V3 and image-filter YOLO (IF-YOLO) was evaluated. The results show that IF-YOLO maintains excellent performance on sunny days while improving the weakness of YOLO. Increasing the content of the dataset will extend the kinds of species detected by the model, which opens up new avenues for detecting others species in harsh conditions.

Keywords

• Hainan gibbon
• Object detection
• Convolutional neural network
• Species identification