CNN-based ringed seal pelage pattern extraction

Abstract

The topic of this thesis is inspired by the conservation efforts of Saimaa ringed seals, which are in danger of becoming extinct with no appropriate actions. The work aims to develop a fur pattern extraction framework to identify ringed seal individuals. This pattern is unique to each individual and as a result, it can be used for identification. In turn, the pelage pattern extraction algorithm is the key part of the identification and it enables, for example, seal counting and monitoring. The proposed seal pelage pattern extraction is based on UNet Convolutional Neural Network (CNN), which brings efficiency improvement to the developed pattern extractor compared to non-neural approaches. Also, the pipeline includes preprocessing stages such as tone mapping and cropping. Moreover, this thesis contains a comparison of UNet to the other CNN-based method called DeepLab in terms of the stated challenge and an overview of sliding windows processing. The proposed method showed Sørensen–Dice coefficient accuracy equals 0.55 vs 0.23 for the Sato filter based solution and 37% faster computation in the conducted test compared to the previously used non-neural solution in the re-identification pipeline.