Underwater Fish Detection Plays A Crucial Role In Various Domains, Such As Marine Biology, Environmental Monitoring, And Fisheries Management. Traditional Methods For Fish Detection In Underwater Environments Often Suffer From Limitations In Accuracy And Efficiency. In Recent Years, Deep Learning-based Object Detection Models Have Shown Remarkable Success In Various Computer Vision Tasks. This Study Presents An Underwater Fish Detection System Utilizing The YOLO Architecture, A State-of-the-art Deep Learning Framework.
