构建首个卫星视频中暗弱移动车辆检测数据集，解决低辐射条件下检测难题。
SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in Satellite Videos
- 基于遥感图像增强与注意力机制提升暗弱车辆可见性
- 包含99段高清卫星视频，标注了大量低辐射移动车辆
- 适合遥感目标检测、低光照视觉任务研究者使用
卫星视频中的车辆检测与追踪在遥感应用中至关重要。然而，现有数据集统计分析显示，低辐射强度、与背景对比度有限的暗弱车辆标注极少，导致现有方法在低辐射条件下检测效果不佳。本文提出构建首个小而暗的移动车辆（SDM-Car）数据集，由珞珈三号01星采集，包含99段高质量卫星视频，并提供丰富标注。同时，提出一种基于图像增强与注意力机制的方法以提升暗弱车辆检测精度，作为该数据集的基准评估方案。最后，在SDM-Car上评估多个代表性方法并得出深入结论。数据集已公开于https://github.com/TanedaM/SDM-Car。
原文摘要 · Abstract (English)
Vehicle detection and tracking in satellite video is essential in remote sensing (RS) applications. However, upon the statistical analysis of existing datasets, we find that the dim vehicles with low radiation intensity and limited contrast against the background are rarely annotated, which leads to the poor effect of existing approaches in detecting moving vehicles under low radiation conditions. In this paper, we address the challenge by building a \textbf{S}mall and \textbf{D}im \textbf{M}oving Cars (SDM-Car) dataset with a multitude of annotations for dim vehicles in satellite videos, which is collected by the Luojia 3-01 satellite and comprises 99 high-quality videos. Furthermore, we propose a method based on image enhancement and attention mechanisms to improve the detection accuracy of dim vehicles, serving as a benchmark for evaluating the dataset. Finally, we assess the performance of several representative methods on SDM-Car and present insightful findings. The dataset is openly available at https://github.com/TanedaM/SDM-Car.
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