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

TL;DR

This paper introduces the SDM-Car dataset for detecting small, dim, and low-contrast moving vehicles in satellite videos, and proposes an enhancement-based detection method to improve accuracy under challenging conditions.

Contribution

The paper presents a new dataset focused on dim vehicles in satellite videos and a novel detection approach using image enhancement and attention mechanisms.

Findings

The dataset contains 99 high-quality videos with extensive annotations.

The proposed method improves detection accuracy for dim vehicles.

Benchmark results highlight challenges and future directions.

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 https://github.com/TanedaM/SDM-Car.