From snapshots to continuous estimates: Augmenting citizen science with computer vision for fish monitoring

Abstract Monitoring fish movement is essential for understanding population dynamics, informing conservation efforts and supporting fisheries management. Traditional methods, such as visual observations by volunteers, are constrained by time limitations, environmental conditions and labour intensity. Recent advancements in computer vision (CV) and deep learning offer promising solutions for automating fish counting from underwater videos, improving efficiency and data resolution. In this study, we developed and applied a deep learning‐based CV system to monitor river herring ( Alosa spp.) migration, covering all essential steps from field camera deployment, video annotation to model training and in‐season population counting. We assessed the labelling and training efforts required to achieve good model performance and explored the use of importance sampling to correct biases in CV‐based fish counts. Our results demonstrated that CV models trained on a single site and year showed limited generalization to sites or years unseen during training, while models trained on more diverse labelled data generalized better. We also found that the amount of annotations required is related to dataset complexity. When applied for in‐season fish counting, CV efficiently processed season‐long datasets and produced counts consistent with human review, with some moderate differences under migration pulses that can be adjusted by importance sampling. By providing continuous, high‐resolution monitoring throughout the entire migration season, CV counts offer more reliable run size estimates and greater insight into the spawning migration of river herring. This study demonstrates a scalable, cost‐effective and efficient approach with significant potential for addressing complex ecological questions and supporting conservation strategies and resource management.