YOLOV4_CSPBi: Enhanced Land Target Detection Model

Lirong Yin, Lei Wang, Jianqiang Li, Siyu Lu, Jiawei Tian, Zhengtong Yin, Shan Liu and Wenfeng Zheng ()
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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
Jianqiang Li: 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
Zhengtong Yin: College of Resource and Environment Engineering, Guizhou University, Guiyang 550025, China
Shan Liu: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China
Wenfeng Zheng: School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China

Land, 2023, vol. 12, issue 9, 1-17

Abstract: The identification of small land targets in remote sensing imagery has emerged as a significant research objective. Despite significant advancements in object detection strategies based on deep learning for visible remote sensing images, the performance of detecting a small and densely distributed number of small targets remains suboptimal. To address this issue, this study introduces an improved model named YOLOV4_CPSBi, based on the YOLOV4 architecture, specifically designed to enhance the detection capability of small land targets in remote sensing imagery. The proposed model enhances the traditional CSPNet by redefining its channel partitioning and integrating this enhanced structure into the neck part of the YOLO network model. Additionally, the conventional pyramid fusion structure used in the traditional BiFPN is removed. By integrating a weight-based bidirectional multi-scale mechanism for feature fusion, the model is capable of effectively reasoning about objects of various sizes, with a particular focus on detecting small land targets, without introducing a significant increase in computational costs. Using the DOTA dataset as research data, this study quantifies the object detection performance of the proposed model. Compared with various baseline models, for the detection of small targets, its AP performance has been improved by nearly 8% compared with YOLOV4. By combining these modifications, the proposed model demonstrates promising results in identifying small land targets in visible remote sensing images.

Keywords: remote sensing; multi-scale feature fusion; land target detecting; deep learning; CNN; reasoning ability; YOLO network; BiFPN (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:9:p:1813-:d:1244467

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