Underwater Object

Underwater object detection and tracking are crucial for various applications, from marine biology to autonomous underwater vehicles, but are hampered by challenging conditions like poor visibility and complex backgrounds. Current research focuses on developing robust algorithms and models, including convolutional neural networks (CNNs) and transformer architectures, often incorporating techniques like self-supervised learning and multi-modal data fusion (e.g., combining sonar and optical imagery) to improve accuracy and efficiency. These advancements are leading to more reliable and efficient systems for identifying and tracking underwater objects, improving our understanding of marine environments and enabling safer and more effective underwater operations.