Underwater Target

Underwater target recognition aims to identify and locate objects submerged in water using acoustic or visual sensors, crucial for various applications like marine resource management and security. Current research heavily focuses on improving the accuracy and efficiency of deep learning models, particularly convolutional neural networks (CNNs) like YOLOv7 and transformer-based architectures, often enhanced with techniques such as contrastive learning and data augmentation to address challenges posed by noisy underwater environments and limited labeled datasets. The development of large, publicly available datasets and novel algorithms that incorporate multimodal data (acoustic, visual, textual) is driving progress, leading to more robust and interpretable systems for underwater target detection and classification. These advancements have significant implications for diverse fields, including marine biology, environmental monitoring, and maritime security.