Real-Time Runway Detection Using Dual-Modal Fusion of Visible and Infrared Data
Advancements in aviation technology have made intelligent navigation systems essential for improving flight safety and efficiency, particularly in low-visibility conditions. Radar and GPS systems face limitations in bad weather, making visible–infrared sensor fusion a promising alternative. This stu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/4/669 |
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| Summary: | Advancements in aviation technology have made intelligent navigation systems essential for improving flight safety and efficiency, particularly in low-visibility conditions. Radar and GPS systems face limitations in bad weather, making visible–infrared sensor fusion a promising alternative. This study proposes a salient object detection (SOD) method that integrates visible and infrared sensors for robust airport runway detection in complex environments. We introduce a large-scale visible–infrared runway dataset (RDD5000) and develop a SOD algorithm capable of detecting salient targets from unaligned visible and infrared images. To enable real-time processing, we design a lightweight dual-modal fusion network (DCFNet) with an independent–shared encoder and a cross-layer attention mechanism to enhance feature extraction and fusion. Experimental results show that the MobileNetV2-based lightweight version achieves 155 FPS on a single GPU, significantly outperforming previous methods such as DCNet (4.878 FPS) and SACNet (27 FPS), making it suitable for real-time deployment on airborne systems. This work offers a novel and efficient solution for intelligent navigation in aviation. |
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| ISSN: | 2072-4292 |