ResQ-UAV: A Novel Dataset Supporting Robust Recognition for Future Drone Rescue Missions

This paper presents and open-sources ResQ-UAV, a dataset designed for person detection in corrupted videos from the perspective of Unmanned Aerial Vehicles (UAVs). Generated using H.264 bitstream corruption techniques, the dataset aims to simulate realistic non-linear signal impairments encountered in actual image transmission links. By mimicking authentic communication link failures, the images exhibit highly complex degradation characteristics, posing significant challenges for detection algorithms operating under communication-constrained conditions. ResQ-UAV comprises 16,414 frames derived from 32 video sequences, featuring a total of 282,095 meticulously annotated bounding boxes for persons. It encompasses diverse complex scenarios, including urban arterials and suburban areas, across various lighting conditions (day and night). Serving as a dedicated benchmark for evaluating video transmission robustness, this dataset significantly enriches the data resources available for UAV vision in complex transmission environments. Benchmarking results based on various state-of-the-art object detection algorithms demonstrate that ResQ-UAV establishes a rigorous performance baseline for multi-object detection tasks within corrupted video environments. The dataset is poised to provide a reliable data foundation and verification platform for the research and application of critical technologies in disaster rescue operations. Data and code are available at: https://github.com/dj-dengjian/ResQ-UAV.git.