Alignment-Aware and Reliability-Gated Multimodal Fusion for Unmanned Aerial Vehicle Detection Across Heterogeneous Thermal-Visual Sensors explores A multimodal UAV detection system leveraging registration-aware and reliability-gated fusion to improve detection performance in heterogeneous sensor environments.. Commercial viability score: 7/10 in UAV Detection.
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Ishrat Jahan
Shahjalal University of Science & Technology
Molla E Majid
Qatar Foundation
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Enhancing UAV detection with multimodal sensor fusion addresses pressing security concerns in regulated airspace, enabling safer and more effective drone operations.
Build a software tool that integrates seamlessly into existing security or traffic management systems at airports and other critical infrastructure.
This system could replace less effective rule-based UAV detection systems that struggle in dynamic environmental conditions, offering a more adaptive solution.
The market for UAV detection and airspace security is expanding rapidly due to increased drone usage. Airports, military bases, and sensitive public areas will pay for effective UAV detection solutions.
Create an enhanced airspace monitoring system for airports using thermal-visual fusion to detect rogue UAVs with high precision.
The paper introduces two fusion strategies, RGIF and RGMAF, to better align and integrate thermal and visual sensor data for UAV detection. RGIF uses Enhanced Correlation Coefficient-based alignment and filtering, while RGMAF weights sensor inputs based on reliability, improving detection accuracy.
Tested on a dataset of over 147,000 annotated frames from various sensor modalities; fusion strategies improved detection metrics significantly over single-modal approaches.
The integration process may be complex due to the heterogeneity of sensor data, and real-world performance can be affected by unpredictable environmental factors.