Using Computer Vision to Count Fish Populations (and Monitor Environmental Health)

Jamie Shaffer, a data scientist based in Washington state, explores the application of deep learning, specifically object detection, to count salmon passing through fish ladders in the Pacific Northwest. This region's salmon populations are crucial for environmental assessment, and the task of counting them is traditionally labor-intensive, involving trained experts to manually review video recordings. Shaffer utilizes the YOLO v5 model for its speed and accuracy, leveraging open-source tools like Roboflow for data augmentation and model training. Despite challenges such as varying lighting and image quality, the model achieves a mean Average Precision ([email protected]) of 70%, suggesting that machine learning can effectively assist in fish counting. This approach not only streamlines the process but also provides valuable data for managing fishing seasons and dam operations, highlighting the readiness of AI for real-world environmental applications.