TMRGBT-D2D: A Temporal Misaligned RGB-Thermal Dataset for Drone-to-Drone Target Detection

In the field of drone-to-drone detection tasks, the issue of fusing temporal information with infrared and visible light data for detection has been rarely studied. This paper presents the first temporal misaligned rgb-thermal dataset for drone-to-drone target detection, named TMRGBT-D2D. The dataset covers various lighting conditions (i.e., high-light scenes captured during the day, medium-light and low-light scenes captured at night, with night scenes accounting for 38.8% of all data), different scenes (sky, forests, buildings, construction sites, playgrounds, roads, etc.), different seasons, and different locations, consisting of a total of 42,624 images organized into sequential frames extracted from 19 RGB-T video pairs. Each frame in the dataset has been meticulously annotated, with a total of 94,323 annotations. Except for drones that cannot be identified under extreme conditions, infrared and visible light annotations are one-to-one corresponding. This dataset presents various challenges, including small object detection (the average size of objects in visible light images is approximately 0.02% of the image area), motion blur caused by fast movement, and detection issues arising from imaging differences between different modalities. To our knowledge, this is the first temporal misaligned rgb-thermal dataset for drone-to-drone target detection, providing convenience for research into rgb-thermal image fusion and the development of drone target detection.