Underwater Insights: Computer Vision Techniques for Fish Behavior Detection

This paper presents a comprehensive review of computer vision techniques for detecting fish behavior in underwater environments. The study systematically analyzes methodologies across image processing, object detection, and motion tracking to enhance the clarity, precision, and dynamic observation of fish activities. By employing a robust literature search strategy and applying rigorous inclusion criteria, studies with strong methodological rigor, clear reporting of results, and demonstrated effectiveness in real-world underwater settings were selected. Key parameters such as accuracy, robustness, scalability, computational efficiency, and generalizability are used to evaluate these techniques. The findings highlight the advancements in adaptive algorithms, semi-supervised learning, and edge computing solutions that enhance real-time analysis and reduce dependency on extensive annotated datasets. This review provides a foundational understanding of the current state and future directions in underwater fish behavior detection, emphasizing the need for integrated multimodal data and collaborative research efforts.