Oyster Detection

Oyster detection research focuses on developing automated, non-destructive methods for monitoring oyster populations, crucial for ecosystem health and aquaculture. Current efforts leverage computer vision, employing object detection models like YOLO, trained on both real-world and synthetically generated underwater imagery to improve accuracy and reduce the need for labor-intensive manual surveys. The use of simulation environments and techniques like stable diffusion to create high-quality synthetic training data is a key trend, addressing the challenges of data acquisition in difficult underwater settings. These advancements promise more efficient and effective monitoring of oyster reefs, supporting conservation and sustainable management practices.