Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Deep Learning-based Image Data Processing and Archival System for Object Detection of Endangered Species

  • Choe, Dea-Gyu (Department of Computer Science, Sangmyung University) ;
  • Kim, Dong-Keun (Department of Human-Centered Artificial Intelligence, Sangmyung University)
  • Received : 2020.10.31
  • Accepted : 2020.12.22
  • Published : 2020.12.31
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Abstract

It is important to understand the exact habitat distribution of endangered species because of their decreasing numbers. In this study, we build a system with a deep learning module that collects the image data of endangered animals, processes the data, and saves the data automatically. The system provides a more efficient way than human effort for classifying images and addresses two problems faced in previous studies. First, specious answers were suggested in those studies because the probability distributions of answer candidates were calculated even if the actual answer did not exist within the group. Second, when there were more than two entities in an image, only a single entity was focused on. We applied an object detection algorithm (YOLO) to resolve these problems. Our system has an average precision of 86.79%, a mean recall rate of 93.23%, and a processing speed of 13 frames per second.

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