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MSCANet: A multi-scale context-aware network for remote sensing object detection

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Abstract

With the rapid development of remote sensing technology and the widespread application of remote sensing images, remote sensing object detection has become a hot research direction. However, we observe three primary challenges in remote sensing object detection: scale variations, small objects, and complex backgrounds. To address these challenges, we propose a novel detector, he Multi-Scale Context-Aware Network (MSCANet). First, we introduce a Multi-Scale Fusion Module (MSFM) that provides various scales of receptive fields to extract contextual information of objects at different scales adequately. Second, the Multi-Scale Guidance Module (MSGM) is proposed, which fuses deep and shallow feature maps from multiple scales, reducing the loss of feature information in small objects. Finally, we introduce the Context-Aware DownSampling Module (CADM). It dynamically adjusts context information weights at different scales, effectively reducing interference from complex backgrounds. Experimental results demonstrate that the proposed MSCANet achieves superior performance results with mean average precision (mAP) of 97.1% and 73.4% on the challenging RSOD and DIOR datasets, respectively, which indicates that the proposed network is suitable for remote sensing object detection and is of a great reference value.

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No datasets were generated or analysed during the current study.

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Funding

This work was supported by the Project of Research and Develop the Key Technology of Mine Explosion-proof Pure Electric Transport Vehicle. Key Research and Development Projects in Anhui Province (202004 b11020029).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan Anhui, 232001, China

    Huaping Zhou, Weidong Liu, Kelei Sun, Jin Wu & Tao Wu

  2. Artificial Intelligence Research Institute, Hefei Comprehensive National Science Center, Hefei, 230000, China

    Huaping Zhou

Authors
  1. Huaping Zhou
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  2. Weidong Liu
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  4. Jin Wu
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  5. Tao Wu
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Contributions

Huaping Zhou and Weidong Liu wrote the main manuscript text and designed the remote sensing object detection model. Kelei Sun performed the data processing and analysis. Jin Wu and Tao Wu prepared Figs. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 and 17. All authors reviewed the manuscript.

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Correspondence to Weidong Liu.

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Communicated by Hassan Babaie.

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Zhou, H., Liu, W., Sun, K. et al. MSCANet: A multi-scale context-aware network for remote sensing object detection. Earth Sci Inform 17, 5521–5538 (2024). https://doi.org/10.1007/s12145-024-01447-8

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