SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in Satellite Videos

Zhen Zhang;Tao Peng;Liang Liao;Jing Xiao;Mi Wang
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Abstract

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 letter, we address the challenge by building a small and dim moving 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 .
SDM-Car:用于在卫星视频中检测小型和昏暗移动车辆的数据集
卫星视频中的车辆检测和跟踪在遥感(RS)应用中至关重要。然而,在对现有数据集进行统计分析后,我们发现辐射强度低、与背景对比度有限的昏暗车辆很少被标注,这导致现有方法在低辐射条件下检测移动车辆的效果不佳。在这封信中,我们针对这一挑战,建立了一个由珞珈 3-01 号卫星采集的、包含 99 个高质量视频的小型昏暗移动车辆(SDM-Car)数据集,该数据集对卫星视频中的昏暗车辆进行了大量注释。此外,我们还提出了一种基于图像增强和注意力机制的方法,以提高昏暗车辆的检测精度,作为评估数据集的基准。最后,我们评估了几种具有代表性的方法在 SDM-Car 上的性能,并提出了深入的研究结果。该数据集可在 https://github.com/TanedaM/SDM-Car 上公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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