CGBi_YOLO: Lightweight Land Target Detection Network

Ruiyang Wang, Siyu Lu, Jiawei Tian, Lirong Yin (), Lei Wang, Xiaobing Chen and Wenfeng Zheng ()
Additional contact information
Ruiyang Wang: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China
Siyu Lu: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China
Jiawei Tian: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China
Lirong Yin: Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
Lei Wang: Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
Xiaobing Chen: School of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
Wenfeng Zheng: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China

Land, 2024, vol. 13, issue 12, 1-19

Abstract: Object detection algorithms for optical remote sensing images often face challenges in computational efficiency, particularly when detecting small and densely packed targets. This paper introduces CGBi_YOLO, a novel lightweight land target detection network designed to optimize computational resource utilization while maintaining detection capabilities for small-scale targets. Our approach incorporates an innovative lightweight optimization strategy featuring a new lightweight backbone feature extraction network: CSPGhostNet. This model significantly enhances the detection ability of small objects within optical remote sensing images without increasing computational demands. The efficacy of the proposed model is validated through rigorous experimentation on the DOTA dataset. Compared to the baseline model, CGBi_YOLO achieves a 30% reduction in parameters and a 36% increase in inference speed. The model demonstrates exceptional performance in handling small and densely packed targets within optical remote sensing images, showcasing its potential for real-world applications in fields such as environmental monitoring, urban planning, and disaster management.

Keywords: remote sensing image; object detection; land target detection; deep learning; CGBi_YOLO; lightweight transformation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/12/2060/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/12/2060/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:12:p:2060-:d:1533984

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().