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

TL;DR

This paper evaluates the performance of advanced object detection models on a new satellite imagery dataset focused on tiny objects like cars, ships, and airplanes, highlighting challenges and providing insights for future improvements.

Contribution

It introduces a curated satellite imagery dataset for small object detection and empirically assesses state-of-the-art models' effectiveness in this domain.

Findings

Certain models perform better on small objects in satellite images.

Challenges remain in detecting tiny objects due to limited context.

Insights guide future research in satellite image analysis.

Abstract

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.