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 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 this https URL.
Abstract (translated)
卫星视频中的车辆检测与跟踪在遥感（RS）应用中至关重要。然而，通过对现有数据集的统计分析，我们发现辐射强度低且与背景对比度有限的暗淡车辆很少被标注，这导致现有的方法在低辐射条件下检测移动车辆的效果不佳。本文通过构建一个名为“小而暗移动汽车”（SDM-Car）的数据集来应对这一挑战，该数据集包含大量卫星视频中暗淡车辆的注释，并由珞珈3-01号卫星收集，包含了99个高质量视频。此外，我们提出了一种基于图像增强和注意力机制的方法，以提高对暗淡车辆的检测精度，并作为评估数据集的标准。最后，我们在SDM-Car上评估了几种代表性方法的表现并提出了有洞察力的发现。该数据集可公开访问，网址为：[此 https URL]。 请注意，原文中的URL需要替换为实际可以访问的地址。
URL
https://arxiv.org/abs/2412.18214
