针对卫星图像小目标检测，构建3000张数据集并实证评估主流模型性能。
Analysis of Object Detection Models for Tiny Object in Satellite Imagery: A Dataset-Centric Approach
- 构建3000张卫星图像数据集，涵盖车、船、飞机三类小目标。
- 在SAT-MTB数据集上使用ByteTrack实现卫星视频目标跟踪。
- 为遥感小目标检测提供可复现的基准测试与方法参考。
近年来，基于深度学习的目标检测算法在计算机视觉任务中取得显著进展，尤其在目标检测、跟踪和分割方面。本文聚焦于卫星图像中的小目标检测（SOD）领域，指出其因成像范围广、目标分布不均及鸟瞰视角下外观多变而带来的独特挑战。传统检测模型因上下文信息有限和类别不平衡，难以有效识别小目标。为此，本研究构建了一个包含3000张图像的数据集，涵盖卫星图像中的汽车、船舶和飞机。通过实证评估主流检测模型，旨在深入理解小目标检测在卫星应用中的表现。此外，还利用ByteTrack算法在SAT-MTB数据集上开展卫星视频目标跟踪研究。实验结果揭示了当前先进模型在小目标检测中的有效性，为未来卫星图像分析技术发展奠定基础。
原文摘要 · Abstract (English)
In recent years, significant advancements have been made in deep learning-based object detection algorithms, revolutionizing basic computer vision tasks, notably in object detection, tracking, and segmentation. This paper delves into the intricate domain of Small-Object-Detection (SOD) within satellite imagery, highlighting the unique challenges stemming from wide imaging ranges, object distribution, and their varying appearances in bird's-eye-view satellite images. Traditional object detection models face difficulties in detecting small objects due to limited contextual information and class imbalances. To address this, our research presents a meticulously curated dataset comprising 3000 images showcasing cars, ships, and airplanes in satellite imagery. Our study aims to provide valuable insights into small object detection in satellite imagery by empirically evaluating state-of-the-art models. Furthermore, we tackle the challenges of satellite video-based object tracking, employing the Byte Track algorithm on the SAT-MTB dataset. Through rigorous experimentation, we aim to offer a comprehensive understanding of the efficacy of state-of-the-art models in Small-Object-Detection for satellite applications. Our findings shed light on the effectiveness of these models and pave the way for future advancements in satellite imagery analysis.
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