Counting seals from the ground, especially on remote islands where most breed, is difficult and dangerous, and manually counting from aerial photos is time-consuming and error-prone. This research proposes an automated method of counting elephant seals using machine learning. Drone images were collected from A˜no Nuevo Reserve, California, US, during the 2022 and 2023 winter breeding seasons.

The system automatically created orthophotos from drone images, made predictions using a single-stage object detection model from tiles, detected and removed duplicate predictions, classified the seals into males, females, and pups, and mapped the predictions back to the orthophotos as labeled bounding boxes. An optional active learning component also allowed human reviewers to make corrections in a UI and edits could be automatically turned into new training data to improve future surveys. In an examination of the largest aggregation on the Mainland, the model found 99.4% of females, 97.8% of males, and 97.0% of pups. The whole pipeline, including model training, can be run on a laptop, and it can be utilized in remote field sites where there is no internet access.