Underwater Object Detection

Underwater object detection aims to automatically identify and locate objects within underwater imagery, a challenging task due to poor visibility and complex environments. Current research heavily utilizes deep learning, focusing on Convolutional Neural Networks (CNNs) like YOLO and EfficientDet, often incorporating image enhancement techniques and domain adaptation strategies to improve accuracy and robustness across diverse underwater conditions. This field is crucial for various applications, including marine resource management, environmental monitoring, and autonomous underwater vehicle navigation, with ongoing efforts to improve detection accuracy, efficiency, and generalization capabilities.