Detection Fire in Camera RGB-NIR

• URL: http://arxiv.org/abs/2512.23594v1
• Date: Mon, 29 Dec 2025 16:48:24 GMT
• Title: Detection Fire in Camera RGB-NIR
• Authors: Nguyen Truong Khai, Luong Duc Vinh,
• Abstract summary: This report presents an additional NIR dataset, a two-stage detection model, and Patched-YOLO.<n>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.<n>Finally, to improve fire detection in RGB images, especially for small and distant objects, we introduce Patched-YOLO.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• SCU-CGAN: Enhancing Fire Detection through Synthetic Fire Image Generation and Dataset Augmentation [6.933111681872053]
  We propose the SCU-CGAN model, which integrates U-Net, CBAM, and an additional discriminator to generate realistic fire images from nonfire images.<n>SCU-CGAN achieved a 41.5% improvement in KID score compared to CycleGAN, demonstrating the superior quality of the generated fire images.<n>Experiments demonstrate that the augmented dataset significantly improves the accuracy of fire detection models without altering their structure.
  arXiv  Detail & Related papers  (2025-12-09T08:38:11Z)
• YOLO-FireAD: Efficient Fire Detection via Attention-Guided Inverted Residual Learning and Dual-Pooling Feature Preservation [5.819675225521611]
  This study propose You Only Look Once for Fire Detection with Attention-guided Inverted Residual and Dual-pooling Downscale Fusion (YOLO-FireAD)<n> Attention-guided Inverted Residual Block (AIR) integrates hybrid channel-spatial attention with inverted residuals to adaptively enhance fire features and suppress environmental noise.<n>Dual Pool Downscale Fusion Block (DPDF) preserves multi-scale fire patterns through learnable fusion of max-average pooling outputs.
  arXiv  Detail & Related papers  (2025-05-27T08:29:07Z)
• Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models [7.222438368943405]
  This study underscores Transfer Learning's (TL) significance in boosting the performance of object detectors for identifying wildfire smoke and flames.<n>We explore a two-stage cascaded TL method, utilizing D-Fire or FASDD as initial stage target datasets and AFSE as the subsequent stage.<n>Through fine-tuning, TL significantly enhances detection precision, achieving up to 79.2% mean Average Precision (mAP@0.5), reduces training time, and increases model generalizability.
  arXiv  Detail & Related papers  (2025-01-15T08:04:44Z)
• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector [60.42293239557962]
  We propose SpirDet, a novel approach for efficient detection of infrared small targets. We employ a new dual-branch sparse decoder to restore the feature map. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2024-02-08T05:06:14Z)
• Constructing a Real-World Benchmark for Early Wildfire Detection with the New PYRONEAR-2025 Dataset [0.0]
  PYRONEAR-2025 is a new dataset composed of both images and videos, allowing for the training and evaluation of smoke plume detection models.<n>The data is sourced from: (i) web-scraped videos of wildfires from public networks of cameras for wildfire detection in-the-wild, (ii) videos from our in-house network of cameras, and (iii) a small portion of synthetic and real images.<n>This dataset includes around 150,000 manual annotations on 50,000 images, covering 640 wildfires.
  arXiv  Detail & Related papers  (2024-02-08T02:01:36Z)
• Long-Tailed 3D Detection via Multi-Modal Fusion [58.89765900064689]
  We study the problem of Long-Tailed 3D Detection (LT3D), which evaluates all annotated classes, including those in-the-tail.<n>We point out that rare-class accuracy is particularly improved via multi-modal late fusion (MMLF) of independently trained uni-modal LiDAR and RGB detectors.<n>Our MMLF significantly outperforms prior work for LT3D, particularly improving on the six rarest classes from 12.8 to 20.0 mAP!
  arXiv  Detail & Related papers  (2023-12-18T07:14:25Z)
• Fire Detection From Image and Video Using YOLOv5 [0.0]
  An improved YOLOv5 fire detection deep learning algorithm is proposed. Fire-YOLOv5 attains excellent results compared to state-of-the-art object detection networks. When the input image size is 416 x 416 resolution, the average detection time is 0.12 s per frame.
  arXiv  Detail & Related papers  (2023-10-10T06:37:03Z)
• Enhancing Low-Light Images Using Infrared-Encoded Images [81.8710581927427]
  Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss. We propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter.
  arXiv  Detail & Related papers  (2023-07-09T08:29:19Z)
• Fewer is More: Efficient Object Detection in Large Aerial Images [59.683235514193505]
  This paper presents an Objectness Activation Network (OAN) to help detectors focus on fewer patches but achieve more efficient inference and more accurate results. Using OAN, all five detectors acquire more than 30.0% speed-up on three large-scale aerial image datasets. We extend our OAN to driving-scene object detection and 4K video object detection, boosting the detection speed by 112.1% and 75.0%, respectively.
  arXiv  Detail & Related papers  (2022-12-26T12:49:47Z)
• Image-Based Fire Detection in Industrial Environments with YOLOv4 [53.180678723280145]
  This work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector.
  arXiv  Detail & Related papers  (2022-12-09T11:32:36Z)
• Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning [64.92447072894055]
  Infrared (IR) cameras are robust under adverse illumination and lighting conditions. We propose an algorithm meta-learning framework to improve existing UDA methods. We produce a state-of-the-art thermal detector for the KAIST and DSIAC datasets.
  arXiv  Detail & Related papers  (2021-10-07T02:28:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.