HPRT-YOLO dataset Part1-test

HPRT-YOLO Dataset (A fused UAV maritime search-and-rescue object detection dataset)
This dataset is designed for small-object detection in UAV-based maritime search and rescue (SAR) and water-surface surveillance scenarios. 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.

To improve generalization under complex maritime conditions, we apply random cropping, scaling, flipping, rotation, and Mosaic augmentation during training; augmentation is disabled for validation and inference.

Because the dataset is relatively large, it is uploaded here in separate parts. The following is Part 1, which contains the test set.

Note: This is a curated and fused derivative of public datasets. Users should cite the original datasets and their corresponding papers/pages and comply with the original licenses.