Fish Specimen

Research on fish specimens is rapidly advancing, driven by the need for efficient and accurate methods for analyzing fish populations and individual traits. Current efforts focus on developing and applying computer vision techniques, including deep learning models like convolutional neural networks and transformers, to automate tasks such as fish tracking, counting, species identification, and morphological analysis from images and videos. These advancements are crucial for improving aquaculture practices, understanding fish behavior and ecology, and accelerating biodiversity research, particularly through the creation and utilization of large, annotated datasets. The development of robust and generalizable algorithms is a key challenge, particularly in addressing the complexities of underwater environments and variable image quality.