Manual methods of stock assessment for benthic species are resource-intensive and inefficient. There is a pressing need for an automated system that can reliably detect and classify benthic organisms from underwater images to enable data-driven regulatory decisions. By leveraging deep learning-based computer vision models and building an automated MLOps pipeline, we aimed to detect organisms with high precision and integrate these capabilities into a workflow that can scale across large datasets and varied environments.
