HPRT-YOLO dataset Part2-train

It is formed by integrating two public datasets: SeaDronesSee and An Ensemble Deep Learning Method with Optimised Weights for Drone-Based Water Rescue and Surveillance. • Source Dataset 1: SeaDronesSee It contains 14,211 maritime UAV images with 6 original labels: ignored, swimmer, boat, jetski, life-saving appliance, buoy, and provides an official train/val/test split of 8,914/1,547/3,750.

The data covers diverse coastal/offshore/harbor environments and includes challenges such as complex sea states, glare, and background clutter. • Source Dataset 2: Drone-Based Water Rescue and Surveillance It includes 3,613 images (train/val/test: 2,766/333/514) annotated with 6 classes: human, wind/sup-board, boat, kayak, buoy, sailboat. To build HPRT-YOLO, we performed unified data cleaning, label alignment, and class remapping across the two sources, and obtained a fused dataset with 8 classes: ignored, human, boat, jetski, buoy, life-saving appliance, wind/sup-board, kayak.