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Related Experiment Video

Updated: Feb 28, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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A Multi-Scale Object Detection Network with Integrated Spatial-Channel Collaborative Attention for Remote Sensing

Lijun Ma1, Chengjun Xu2, Kun Jiao1

  • 1College of Energy (College of Modern Shale Gas Industry), Chengdu University of Technology, Chengdu 610059, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
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This study introduces a novel multi-scale object detection network for remote sensing images, improving accuracy for small and large targets. The new model enhances feature representation and reduces computational cost, outperforming existing methods.

Area of Science:

  • Computer Vision
  • Remote Sensing
  • Machine Learning

Background:

  • Current remote sensing object detection models struggle with scale variations, cluttered backgrounds, and small object detection.
  • Fixed-size convolution kernels in feature extraction blur object contours and limit representation.
  • Existing attention mechanisms lack deep integration of spatial and channel features, increasing computational overhead.

Purpose of the Study:

  • To develop a multi-scale object detection network for remote sensing images.
  • To enhance feature perception and representation for multi-scale targets, especially small ones.
  • To improve detection accuracy while reducing computational cost.

Main Methods:

  • Introduced a cross-channel multi-scale feature extraction module (CC-MSFE) for enhanced feature representation.
Keywords:
computational efficiencycross-attention mechanismdeep learningmultiscale feature extractionremote sensing images

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  • Developed a channel-spatial cross-attention mechanism (CSCA) integrating channel attention (CA), spatial attention (SA), and cross-attention fusion (CAFM).
  • Designed a dynamic interaction and joint optimization across spatial and channel dimensions for improved detection.

    • Main Results:

    • Achieved 78.1% mAP on DIOR, 90.6% mAP on HRRSD, and 96.5% mAP on RSOD datasets.
    • Outperformed YOLOv11 by 0.7% and 1.4% on DIOR and HRRSD, respectively.
    • Surpassed YOLOv8 by 2.1% on RSOD, with lower parameter count and computational complexity.

    Conclusions:

    • The proposed network effectively addresses challenges in remote sensing object detection.
    • The integrated spatial-channel collaborative attention significantly improves detection accuracy for multi-scale objects.
    • The model achieves a superior balance between detection performance and computational efficiency.