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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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AMSRDet: An Adaptive Multi-Scale UAV Infrared-Visible Remote Sensing Vehicle Detection Network
Sensors (Basel, Switzerland)
|February 13, 2026
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.
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.
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