Full
Outline
Infrared video satellites serve as critical tools for detecting aerial moving targets，with infrared small target detection technology forming the essential foundation for this capability. The rapid advancement of deep learning has yielded numerous single-frame detection datasets and methodologies，enabling substantial progress in identifying spatially salient targets. However，moving aerial targets captured by infrared satellites typically exhibit low spatial salience and frequently occur in complex scenarios，rendering single-frame detection methods reliant on spatial information ineffective in such challenging conditions. This urgent challenge necessitates the development of multiframe infrared small and dim target detection techniques. A major bottleneck restricting the advancement and practical application of aerial moving target detection technology has been the lack of dedicated datasets for infrared video satellite-based detection，primarily due to the difficulties and high costs associated with data collection and annotation. To address this gap and promote technological development，we construct the first infrared video satellite aerial moving target detection dataset containing many real-world scenarios，called SatVideoIRSDT dataset，and organized the inaugural detection competition based on this dataset.
First，we collect 20 126 frames of real infrared video satellite data from Wuhan No.1 satellite featuring aerial moving targets and annotate 29 757 aerial targets. During the data annotation phase，we design a two-stage annotation method to address the challenge of extremely poor spatial salience of the targets that makes them difficult to distinguish. The first stage is data preannotation，where videos are initially cropped spatially into multiple locally enlarged subvideos. By repeatedly playing these subvideos，we determine the presence of targets in each and annotate their temporal and spatial range information. The second stage involves fine-grained data annotation based on the preannotation results，following a specific workflow： confirming target positions，drawing target boundaries，and filling to generate masks. In this manner，well-annotated real data can be obtained. Then，we integrate two simulated infrared aerial moving target datasets with authentic space-based backgrounds to enhance scenario diversity. The resulting dataset comprises 1 401 real scenarios，122 265 video frames，and 454 116 annotated targets，with mask labels distinguishing different target instances to support detection and tracking research. In the challenge based on this dataset，we establish two core tasks，i.e.，aerial moving target detection and tracking，to advance integrated detection-tracking technology. Corresponding to the two tasks，we design two special evaluation metric systems. Especially，the tracking performance metrics are proposed for the first time，including trajectory completeness rating and trajectory accuracy rating.
Analysis reveals the high complexity of our dataset： The real targets are extremely small and dim，with an average size of no more than 20 pixels and an average signal-to-noise ratio （SNR） of only 3.06. Among them，over 80% of the targets have an SNR below 2. The real videos contain rich dynamic variations of targets，including changes in motion state，grayscale distribution，and morphological size. The background also exhibits complex dynamic changes，including translation，rotation，stretching，and scaling in different directions，and local grayscale variations. Additionally，complex mutual radiation interference exists between targets and backgrounds. The diversity introduced by real-world scenarios and actual systems cannot be easily replicated by simulated data，which also causes our SatVideoIRSDT dataset more challenging than other public datasets. For the detection task，a state-of-the-art baseline algorithm achieved a recall of 0.578 2，precision of 0.495 8，and F1-score of 0.533 8 on this dataset. For the tracking task，a typical baseline algorithm yielded a trajectory completeness rating of only 0.241 9 and trajectory accuracy rating of 0.519 1.
The competition fully validated the practical value and high challenge of our SatVideoIRSDT dataset，providing crucial data support for research on infrared small and dim target detection technology. It also attracted substantial attention to the field，facilitating academic exchange and technological progress. Through this event，we collected numerous outstanding aerial moving target detection and tracking solutions，which have contributed valuable new insights to drive domain development. The dataset is available at https://github.com/TinaLRJ/DeepPro（or Science Data Bank： Infrared video satellite aerial moving target detection dataset）.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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