Document Type

Article

Publication Date

2025

DOI

10.3390/land14051047

Publication Title

Land

Volume

14

Issue

5

Pages

1047 (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.

Rights

© 2025 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The DOTAv1.0 dataset, which serves as a valuable resource for validating the results obtained in this study, is publicly accessible at the following location: https://captain-whu.github.io/DOTA/dataset.html (accessed on 1 March 2025)."

Original Publication Citation

Hu, W. Y., Jiang, X. M., Tian, J. W., Ye, S. T., & Liu, S. (2025). Land target detection algorithm in remote sensing images based on deep learning. Land, 14(5), 1-26, Article 1047. https://doi.org/10.3390/land14051047

ORCID

0000-0002-8040-0367 (Liu)

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