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SD-Net: Spatial Dual Network for Aerial Object Detection

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Abstract

The distribution direction of aerial objects is arbitrary compared to objects in natural images. However, the existing detectors identify and locate the targets by relying on the shared features, which leads to the contradiction of regression and classification tasks. To be specific, the classifier suppresses rotation-sensitive features, while the regressor relies on rotation-variable features. To address the above contradictions, a Spatial Dual Network (SD-Net) is proposed, which consists of two modules: Polarization Dual Pyramid Module (PDPM) and Spatial Coordinate Attention Module (SCAM). In the SCAM module, to be able to capture channel-related features and global spatial features in different directions, an attention module is built with different convolution kernels that slide in both horizontal and vertical directions. In addition, the polarization function in the Polarization Dual Pyramid Module can split features into features suitable for classification and regression tasks for use in the classifier and regressor of the network, enabling more refined detection. The experimental results on three remote sensing datasets (i.e., DOTA, UCAS-AOD, and HRSC2016) demonstrate that the proposed method achieves higher performance on detection tasks while maintaining high efficiency.

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Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 61971006).

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Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, China

    Yangte Gao & Liang Chen

  2. School of Information, North China University of Technology, Beijing, China

    Fukun Bi

  3. Party School of Liaoning Provincial Party Committee, Liaoning Academy of Governance, Shenyang, China

    Xiaoyu Nie

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  1. Yangte Gao
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  2. Fukun Bi
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  3. Liang Chen
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  4. Xiaoyu Nie
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Correspondence to Xiaoyu Nie.

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Cite this article

Gao, Y., Bi, F., Chen, L. et al. SD-Net: Spatial Dual Network for Aerial Object Detection. J Indian Soc Remote Sens 51, 2067–2076 (2023). https://doi.org/10.1007/s12524-023-01750-9

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  • DOI: https://doi.org/10.1007/s12524-023-01750-9

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