mlsanityData to 14 Oct 2026

Analysis of Object Detection Models for Tiny Object in Satellite Imagery: A Dataset-Centric Approach

arXiv 2412.10453 ↗First posted 12 Dec 2024cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 40 of its 59 references that are in the map. First posted on arXiv 12 Dec 2024.

Builds on

40 of 59 references.

References · 40

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Bridging the Scale Gap: Balanced Tiny and General Object Detection in Remote Sensing Imagerycited by 1