Abundance estimation of sea mammals like ice breeding seals (harp and hooded) and coastal seals (grey and harbour) are based on aerial photographic surveys. The Institute of Marine Research (IMR) utilizes aerial images of the sea ice captured from above as a data source to estimate abundance of seal populations.

To achieve this in a reliable and cost-effective manner better automation of this process is needed and will lead to better long-term management of seal populations. Previous activities have demonstrated that there is a potential in using deep learning for this. However, limitations in training data due to incomplete annotations and shifts in sensors and conditions over time are challenges that need to be solved.

For the detection and classification of seals on ice, the datasets will come from both drones and manned aerial surveys. We will start from previous models and extend these to include more species (coastal seal species). Furthermore, we need to improve on these models to better handle incomplete annotations and shifts in sensors and conditions over time, with the aim of limiting false positives, and to account for large fractions of images without seals.