Deep learning-based software for detecting population density of Antarctic birds

• Published in: Computer Science Journal of Moldova
• Main Author: Sinan Uğuz
• Format: Article in Journal/Newspaper
• Language: English
• Published: Vladimir Andrunachievici Institute of Mathematics and Computer Science 2023
• Subjects: deep learning; antarctic birds; remote sensing; population estimation; convolutional neural network; Electronic computers. Computer science; QA75.5-76.95; Antarctic; The Antarctic; Antarc*; Antarctica
• Online Access: https://doi.org/10.56415/csjm.v31.11; https://doaj.org/article/2c6f5900e3db42869a7a7a9a4f9cb12d

Monitoring populations of bird species living in Antarctica with current technologies is critical to the future of habitats on the continent. Studies of bird species living in Antarctica are limited due to climate, challenging geographic conditions, and transportation and logistical constraints. The goal of this study is to develop Deep Learning-based software to determine the population densities of Antarctic penguins and endangered albatrosses. Images of penguins and albatrosses obtained from internet sources were labeled using the segmentation technique. For this purpose, 4144 labeled data were trained with five different convolutional neural network architectures TOOD, YOLOv3, YOLOF, Mask R-CNN, and Sparse R-CNN. The performance of the obtained models was measured using the average precision (AP) metric. The experimental results show that the TOOD-ResNet50 model with 0.73 {$AP^{50}$} detects the Antarctic birds adequately compared to the other models. At the end of the study, a software was developed to detect penguins and albatrosses in real time.