Detection Fire in Camera RGB-NIR

TL;DR

This paper introduces a new NIR dataset, a two-stage detection pipeline combining YOLOv11 and EfficientNetV2-B0, and Patched-YOLO to improve fire detection accuracy, especially at night and for small objects.

Contribution

The paper presents a novel two-stage detection model, an additional NIR dataset, and Patched-YOLO for enhanced fire detection in RGB and NIR images, addressing dataset scarcity and false positives.

Findings

Higher detection accuracy for night-time fire detection.

Reduced false positives from artificial lights.

Improved detection of small and distant fires.

Abstract

Improving the accuracy of fire detection using infrared night vision cameras remains a challenging task. Previous studies have reported strong performance with popular detection models. For example, YOLOv7 achieved an mAP50-95 of 0.51 using an input image size of 640 x 1280, RT-DETR reached an mAP50-95 of 0.65 with an image size of 640 x 640, and YOLOv9 obtained an mAP50-95 of 0.598 at the same resolution. Despite these results, limitations in dataset construction continue to cause issues, particularly the frequent misclassification of bright artificial lights as fire. This report presents three main contributions: an additional NIR dataset, a two-stage detection model, and Patched-YOLO. First, to address data scarcity, we explore and apply various data augmentation strategies for both the NIR dataset and the classification dataset. Second, to improve night-time fire detection accuracy while reducing false positives caused by artificial lights, we propose a two-stage pipeline combining YOLOv11 and EfficientNetV2-B0. The proposed approach achieves higher detection accuracy compared to previous methods, particularly for night-time fire detection. Third, to improve fire detection in RGB images, especially for small and distant objects, we introduce Patched-YOLO, which enhances the model's detection capability through patch-based processing. Further details of these contributions are discussed in the following sections.