2027

Fred, Hilla; Krogh, Mogens Agerbo; Bang Jensen, Britt; Ruotsalainen, Laura; Vielma, Jouni; Pastell, Matti

Detecting individual animals is an important step for building monitoring tools for animal based welfare indicators. Latest advances in foundation models (FM) have shown promising performance in zero-shot segmentation over various domains. Here, we show that an FM Segment Anything Model (SAM) can be used to find rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) in a real-life recirculated aquaculture system (RAS) commercial production environment with a stocking density of 32–40kg/m3, without the need for costly training data. We evaluate two prompting methods over a benchmark containing 88 manually verified images and thousands of masks, comparing these hand-annotated prompts with fully automatic segmentation. We also propose a fast annotation workflow utilizing a lightweight classifier to refine the set of output masks, improving the proportion of good-quality whole fish or head masks for automatic detection from 60.5% to 77.7%, and for hand-annotated prompts from 75.6% to 85.2%.