Computer vision to expand monitoring and accelerate assessment of coastal fish
Informations
- Funding country
Norway- Acronym
- -
- URL
- -
- Start date
- 1/1/2021
- End date
- 12/31/2025
- Budget
- 1,502,322 EUR
Fundings
| Name | Role | Start | End | Amount |
|---|---|---|---|---|
| Marine Resources and the Environment (MARINFORSK) - call 2016 | Grant | - | - | 1,502,322 EUR |
Abstract
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. CoastVision will focus on Atlantic cod, ballan wrasse and corkwing wrasse, all 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 withy 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.