Fire Detection in RGB-NIR Cameras
Research Paper#Computer Vision, Fire Detection, Deep Learning🔬 Research|Analyzed: Jan 3, 2026 18:37•
Published: Dec 29, 2025 16:48
•1 min read
•ArXivAnalysis
This paper addresses the challenge of fire detection, particularly at night, using RGB-NIR cameras. It highlights the limitations of existing models in distinguishing fire from artificial lights and proposes solutions including a new NIR dataset, a two-stage detection model (YOLOv11 and EfficientNetV2-B0), and Patched-YOLO for improved accuracy, especially for small and distant fire objects. The focus on data augmentation and addressing false positives is a key strength.
Key Takeaways
- •Addresses the problem of fire detection in RGB-NIR cameras, particularly at night.
- •Proposes a two-stage detection model to reduce false positives from artificial lights.
- •Introduces Patched-YOLO to improve detection of small and distant fire objects.
- •Emphasizes the importance of data augmentation for improved performance.
Reference / Citation
View Original"The paper introduces a two-stage pipeline combining YOLOv11 and EfficientNetV2-B0 to improve night-time fire detection accuracy while reducing false positives caused by artificial lights."