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AMSRDet: An Adaptive Multi-Scale UAV Infrared-Visible Remote Sensing Vehicle Detection Network.

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  • 1School of Art and Science, Columbia University, New York, NY 10027, USA.

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|February 13, 2026
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Summary

This study introduces AMSRDet, an advanced AI system for detecting vehicles from drone imagery. It effectively handles scale variations and sensor limitations, improving accuracy in complex aerial scenes.

Keywords:
adaptive attentioncross-modal fusioninfrared-visible fusionmulti-scale detectionstate-space modelsunmanned aerial vehicle detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Unmanned Aerial Vehicle (UAV) platforms offer cost-effective vehicle detection for intelligent transportation systems.
  • Detecting small vehicles in complex aerial scenes presents challenges due to scale variations, environmental interference, and single-sensor limitations.

Purpose of the Study:

  • To develop an adaptive multi-scale detection network for robust UAV-based vehicle detection.
  • To fuse infrared (IR) and visible (RGB) modalities for enhanced detection performance.

Main Methods:

  • Introduced AMSRDet (Adaptive Multi-Scale Remote Sensing Detector) with four novel components.
  • Employed a MobileMamba-based dual-stream encoder with Selective State-Space 2D (SS2D) blocks for efficient feature extraction.
  • Integrated a Cross-Modal Global Fusion (CMGF) module for capturing global dependencies and suppressing noise.
  • Utilized a Scale-Coordinate Attention Fusion (SCAF) module and a Separable Dynamic Decoder for improved multi-scale feature integration and scale-adaptive predictions.

Main Results:

  • AMSRDet achieved 45.8% mAP@0.5:0.95 and 81.2% mAP@0.5 on the DroneVehicle dataset.
  • The system operated at 68.3 Frames Per Second (FPS) with 28.6 million parameters and 47.2 GFLOPs.
  • Outperformed twenty state-of-the-art detectors, including YOLOv12, DEIM, and Mamba-YOLO, with significant mAP improvements.
  • Demonstrated strong generalization on the Camera-vehicle dataset, achieving 52.3% mAP without fine-tuning.

Conclusions:

  • AMSRDet provides a robust and efficient solution for UAV-based vehicle detection in complex remote sensing scenarios.
  • The proposed fusion of IR and RGB modalities, coupled with novel architectural components, significantly enhances detection accuracy and generalization.
  • The framework addresses key challenges in scale variation and sensor limitations, paving the way for improved intelligent transportation systems.