Automated counting of nesting Adélie penguins at Cape Hallett, Ross Sea, Antarctica, using very high-resolution drone images and the YOLOv11 object detection model

Abstract

Adélie penguins (Pygoscelis adeliae) are vital bioindicators of environmental change in Antarctica. This study evaluated the state-of-the-art You Only Look Once version 11 (YOLOv11) deep learning-based object detection model and very high-resolution (VHR) drone images to comprehensively census nesting Adélie penguins on Cape Hallett, Ross Sea, Antarctica. A total of 554 drone images, acquired from four independent flights in November 2017, were mosaicked at a spatial resolution of approximately 7.1 mm and then segmented into small-size image tiles for nesting penguin detection. An overlap was applied between adjacent small image tiles to prevent the loss of detected nesting penguins caused by segmentation cuts. The detection results from the overlapping areas in adjacent image tiles were consolidated using a predefined distance threshold of 0.1 m to eliminate duplicate detections. The deep learning-based object detection model achieved a detection precision of 95.3%, a recall of 97.6% at a confidence level of 0.5 and an estimated population of 47 340 nesting penguins. The derived population estimate closely agreed with results from an aerial survey conducted in the same year. Some nesting penguins were missed in dark land areas where colour contrast was low, and nesting-penguin-like rocks, shadows and wet areas caused false detections. Nevertheless, the approach demonstrated high accuracy, efficiency and scalability. This study highlights the potential of combining VHR drone images and deep learning-based object detection for low-disturbance penguin monitoring, providing data to support conservation strategies and to assess the impacts of climate change and human activities on Antarctic ecosystems.