Underwater Detection

Underwater object detection aims to automatically identify and locate objects within aquatic environments, a challenging task due to factors like poor visibility and complex backgrounds. Current research focuses on improving the accuracy and efficiency of deep learning models, particularly variations of YOLO and R-CNN architectures, often incorporating image enhancement techniques to mitigate the effects of water turbidity and low light. These advancements are crucial for applications ranging from marine resource management and ecological monitoring to infrastructure inspection and underwater robotics, enabling more efficient and effective data analysis in challenging underwater settings.