Remote sensing image recognition based on YOLOv5

Shaobo Jiang

doi:10.1117/12.2684737

21 July 2023 Remote sensing image recognition based on YOLOv5

Shaobo Jiang

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127173E (2023) https://doi.org/10.1117/12.2684737
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

Currently, remote sensing technology is developing rapidly, and many satellites and airborne sensors can provide high spatial resolution optical images, especially visible light images, which can provide the most intuitive and clear information of the earth's surface. Therefore, how to effectively process large-scale data information and obtain better detection results has become an important challenge in remote sensing image target detection research. This paper proposes an improvement to the target detection algorithm based on YOLOv5. In the input stage, dynamic anchor boxes are introduced to improve the instability of generating anchor boxes based on K-Means clustering, thus reducing model loss and convergence difficulties. In the backbone network stage, the recognition accuracy of small targets and cohesive targets with mutual occlusion is improved by introducing attention mechanisms.

Citation Download Citation

Shaobo Jiang "Remote sensing image recognition based on YOLOv5", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127173E (21 July 2023); https://doi.org/10.1117/12.2684737

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available