This study presents an innovative approach to fish target detection by leveraging the advanced capabilities of YOLOv9 and Faster R-CNN, two of the leading convolutional neural network models designed for real-time object detection. With the aim of enhancing the accuracy and efficiency of underwater fish detection in diverse and complex aquatic environments, this research compares the performance of YOLOv9 and Faster R-CNN in terms of detection speed, accuracy, and reliability. Utilizing a comprehensive dataset of underwater images featuring various fish species in different settings, the study meticulously evaluates each model's ability to accurately identify and locate fish targets amidst background noise and varying visibility conditions. The findings demonstrate that YOLOv9, known for its speed and lightweight architecture, excels in scenarios requiring real-time detection, while Faster R-CNN, with its emphasis on detection accuracy through region proposal mechanisms, shows superior precision in complex image contexts. This comparative analysis not only sheds light on the strengths and weaknesses of each model in fish detection tasks but also provides valuable insights for the development of more effective and adaptive aquatic life monitoring systems. Through this exploration, the research contributes to the advancement of marine biology studies, sustainable fishing practices, and the preservation of aquatic ecosystems by offering a robust tool for accurate and efficient fish detection.

Keywords:  YOLOv9, Faster R-CNN, Real-time object detection, Underwater image analysis, Aquatic environments