Small Object Detection Based on Deep Learning for Remote Sensing: A Comprehensive Review

Author:

Wang Xuan1ORCID,Wang Aoran1,Yi Jinglei1,Song Yongchao1,Chehri Abdellah2ORCID

Affiliation:

1. School of Computer and Control Engineering, Yantai University, Yantai 264005, China

2. Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada

Abstract

With the accelerated development of artificial intelligence, remote-sensing image technologies have gained widespread attention in smart cities. In recent years, remote sensing object detection research has focused on detecting and counting small dense objects in large remote sensing scenes. Small object detection, as a branch of object detection, remains a significant challenge in research due to the image resolution, size, number, and orientation of objects, among other factors. This paper examines object detection based on deep learning and its applications for small object detection in remote sensing. This paper aims to provide readers with a thorough comprehension of the research objectives. Specifically, we aggregate the principal datasets and evaluation methods extensively employed in recent remote sensing object detection techniques. We also discuss the irregularity problem of remote sensing image object detection and overview the small object detection methods in remote sensing images. In addition, we select small target detection methods with excellent performance in recent years for experiments and analysis. Finally, the challenges and future work related to small object detection in remote sensing are highlighted.

Funder

Natural Science Foundation of Shandong Province

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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