Computer vision to expand monitoring and accelerate assessment of coastal fish (CoastVision)

About the project

It is now common to use underwater cameras to study and monitor coastal fish populations. Currently, human experts manually identify, size and count fish, frame by frame. This represents a bottleneck for upscaling deployment and data analysis.

CoastVision will apply deep learning to develop automated detection and sizing of coastal fish caught on camera. The computer vision will also be trained to identify fish in the wild by their natural “barcodes” that distinguish species, sexes and individuals, such as differences in body shape and skin coloration patterns. Individual identification and reliable re-identification is the most innovative and novel aspect of CoastVision and will open new opportunities to study behaviour, growth and survival of fish in their natural habitat.

We focus on Atlantic cod, salmon, and wrasses, all ecologically and commercially important species with complex, high-contrast skin patterns. This feature will be the final step in a fully automated video analysis pipeline that will identify, track, size and count fish in video feeds from long term monitoring stations. The pipeline will be integrated into ongoing surveys and case studies whose main objective is to better understand the factors that affect the reproduction, recruitment, and survival of commercially and ecologically important coastal fishes. Further, CoastVision will support studies on short- and long-term temporal dynamics of fish communities, including detecting as the arrival of invasive species, distribution shifts and altered animal behaviour associated with climate change or other environmental stressors.

Widespread adoption of camera-based monitoring with integrated computer vision will revolutionize our ability to observe, understand and respond to ecological change at scales far more refined than is currently possible.

CoastVision is collaborating with the research projects ShareVision and ARVEN.