Fire Dynamic Vision: Image Segmentation and Tracking for Multi-Scale Fire and Plume Behavior

• URL: http://arxiv.org/abs/2408.08984v1
• Date: Fri, 16 Aug 2024 19:25:19 GMT
• Title: Fire Dynamic Vision: Image Segmentation and Tracking for Multi-Scale Fire and Plume Behavior
• Authors: Daryn Sagel, Bryan Quaife,
• Abstract summary: Wildfires highlight the need for accurate fire and plume spread models. We introduce an approach that effectively isolates and tracks fire and plume behavior across various spatial and temporal scales. Our method combines image segmentation and graph theory to delineate fire fronts and plume boundaries.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing frequency and severity of wildfires highlight the need for accurate fire and plume spread models. We introduce an approach that effectively isolates and tracks fire and plume behavior across various spatial and temporal scales and image types, identifying physical phenomena in the system and providing insights useful for developing and validating models. Our method combines image segmentation and graph theory to delineate fire fronts and plume boundaries. We demonstrate that the method effectively distinguishes fires and plumes from visually similar objects. Results demonstrate the successful isolation and tracking of fire and plume dynamics across various image sources, ranging from synoptic-scale ($10^4$-$10^5$ m) satellite images to sub-microscale ($10^0$-$10^1$ m) images captured close to the fire environment. Furthermore, the methodology leverages image inpainting and spatio-temporal dataset generation for use in statistical and machine learning models.

Related papers

• Automated Image-Based Identification and Consistent Classification of Fire Patterns with Quantitative Shape Analysis and Spatial Location Identification [1.799933345199395]
  Fire patterns, consisting of fire effects that offer insights into fire behavior and origin, are traditionally classified based on investigators' visual observations. This study proposes a framework for quantitative fire pattern classification to support fire investigators, aiming for consistency and accuracy. The framework's classification results achieve 93% precision on synthetic data and 83% on real fire patterns.
  arXiv  Detail & Related papers  (2024-10-30T15:15:41Z)
• Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion Prior [63.64088590653005]
  We propose Diff-Mosaic, a data augmentation method based on the diffusion model. We introduce an enhancement network called Pixel-Prior, which generates highly coordinated and realistic Mosaic images. In the second stage, we propose an image enhancement strategy named Diff-Prior. This strategy utilizes diffusion priors to model images in the real-world scene.
  arXiv  Detail & Related papers  (2024-06-02T06:23:05Z)
• FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion [4.038140001938416]
  We present a training-free, diffusion-based framework designed to generate realistic wildfire images with paired ground truth. Our framework uses augmented masks, sampled from real wildfire data, and applies Perlin noise to guide the generation of realistic flames. We evaluate the generated images using normalized Frechet Inception Distance, CLIP Score, and a custom CLIP Confidence metric.
  arXiv  Detail & Related papers  (2024-03-06T04:59:38Z)
• OsmLocator: locating overlapping scatter marks with a non-training generative perspective [48.50108853199417]
  Locating overlapping marks faces many difficulties such as no texture, less contextual information, hallow shape and tiny size. Here, we formulate it as a optimization problem on clustering-based re-visualization from a non-training generative perspective. We especially built a dataset named 2023 containing hundreds of scatter images with different markers and various levels of overlapping severity, and tested the proposed method and compared it to existing methods.
  arXiv  Detail & Related papers  (2023-12-18T12:39:48Z)
• Foveation in the Era of Deep Learning [6.602118206533142]
  We introduce an end-to-end differentiable foveated active vision architecture that leverages a graph convolutional network to process foveated images. Our model learns to iteratively attend to regions of the image relevant for classification. We find that our model outperforms a state-of-the-art CNN and foveated vision architectures of comparable parameters and a given pixel or budget.
  arXiv  Detail & Related papers  (2023-12-03T16:48:09Z)
• Curved Diffusion: A Generative Model With Optical Geometry Control [56.24220665691974]
  The influence of different optical systems on the final scene appearance is frequently overlooked. This study introduces a framework that intimately integrates a textto-image diffusion model with the particular lens used in image rendering.
  arXiv  Detail & Related papers  (2023-11-29T13:06:48Z)
• Detecting Generated Images by Real Images Only [64.12501227493765]
  Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace.
  arXiv  Detail & Related papers  (2023-11-02T03:09:37Z)
• ExposureDiffusion: Learning to Expose for Low-light Image Enhancement [87.08496758469835]
  This work addresses the issue by seamlessly integrating a diffusion model with a physics-based exposure model. Our method obtains significantly improved performance and reduced inference time compared with vanilla diffusion models. The proposed framework can work with both real-paired datasets, SOTA noise models, and different backbone networks.
  arXiv  Detail & Related papers  (2023-07-15T04:48:35Z)
• Experimental Large-Scale Jet Flames' Geometrical Features Extraction for Risk Management Using Infrared Images and Deep Learning Segmentation Methods [0.9236074230806579]
  Jet fires are relatively small and have the least severe effects among the diverse fire accidents that can occur in industrial plants. This research work explores the application of deep learning models in an alternative approach that uses the semantic segmentation of jet fires flames.
  arXiv  Detail & Related papers  (2022-01-20T00:50:41Z)
• Attention on Classification for Fire Segmentation [82.75113406937194]
  We propose a Convolutional Neural Network (CNN) for joint classification and segmentation of fire in images. We use a spatial self-attention mechanism to capture long-range dependency between pixels, and a new channel attention module which uses the classification probability as an attention weight.
  arXiv  Detail & Related papers  (2021-11-04T19:52:49Z)
• Unsupervised Image-generation Enhanced Adaptation for Object Detection in Thermal images [4.810743887667828]
  This paper proposes an unsupervised image-generation enhanced adaptation method for object detection in thermal images. To reduce the gap between visible domain and thermal domain, the proposed method manages to generate simulated fake thermal images. Experiments demonstrate the effectiveness and superiority of the proposed method.
  arXiv  Detail & Related papers  (2020-02-17T04:53:30Z)

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.