A framework labels underwater images by physical characteristics to group them semantically and evaluate object detection performance across real domain factors.
A structured review of underwater object detection challenges and solutions: From traditional to large vision language models
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YOLO-MD improves underwater marine debris detection by adding a Dual-Branch Convolutional Enhanced Self-Attention module, a lightweight shift operation, and SFG-Loss for class imbalance, achieving 0.875 precision and 0.849 mAP50 on the UODM dataset.
citing papers explorer
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Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection
A framework labels underwater images by physical characteristics to group them semantically and evaluate object detection performance across real domain factors.
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A Marine Debris Detection Framework for Ocean Robots via Self-Attention Enhancement and Feature Interaction Optimization
YOLO-MD improves underwater marine debris detection by adding a Dual-Branch Convolutional Enhanced Self-Attention module, a lightweight shift operation, and SFG-Loss for class imbalance, achieving 0.875 precision and 0.849 mAP50 on the UODM dataset.