Land Target Detection Algorithm in Remote Sensing Images Based on Deep Learning

Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye and Shan Liu ()
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Wenyi Hu: College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China
Xiaomeng Jiang: College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China
Jiawei Tian: Department of Computer Science and Engineering, Hanyang University, Ansan 15577, Republic of Korea
Shitong Ye: School of Artificial Intelligence, Guangzhou Huashang College, Guangzhou 511300, China
Shan Liu: Department of Modelling, Simulation, and Visualization Engineering, Old Dominion University, Norfolk, VA 23529, USA

Land, 2025, vol. 14, issue 5, 1-26

Abstract: Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and Shape-IoU to improve detection accuracy while employing depthwise separable convolution to reduce model complexity. The proposed architecture was evaluated systematically on the DOTAv1.0 dataset, and our results show that YOLOv5s-CACSD achieved a 91.0% mAP@0.5, marking a 2% improvement over the original YOLOv5s. Additionally, it reduced model parameters and computational complexity by 0.9 M and 2.9 GFLOPs, respectively. These results demonstrate the enhanced detection performance and efficiency of the YOLOv5s-CACSD model, making it suitable for practical applications in land target detection for remote sensing imagery.

Keywords: remote sensing; deep learning; land target detecting; YOLOv5 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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