Obscured Wildfire Flame Detection By Temporal Analysis of Smoke Patterns Captured by Unmanned Aerial Systems

• URL: http://arxiv.org/abs/2307.00104v1
• Date: Fri, 30 Jun 2023 19:45:43 GMT
• Title: Obscured Wildfire Flame Detection By Temporal Analysis of Smoke Patterns Captured by Unmanned Aerial Systems
• Authors: Uma Meleti and Abolfazl Razi
• Abstract summary: This research paper addresses the challenge of detecting obscured wildfires in real-time using drones equipped only with RGB cameras. We propose a novel methodology that employs semantic segmentation based on the temporal analysis of smoke patterns in video sequences.
• Score: 0.799536002595393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research paper addresses the challenge of detecting obscured wildfires (when the fire flames are covered by trees, smoke, clouds, and other natural barriers) in real-time using drones equipped only with RGB cameras. We propose a novel methodology that employs semantic segmentation based on the temporal analysis of smoke patterns in video sequences. Our approach utilizes an encoder-decoder architecture based on deep convolutional neural network architecture with a pre-trained CNN encoder and 3D convolutions for decoding while using sequential stacking of features to exploit temporal variations. The predicted fire locations can assist drones in effectively combating forest fires and pinpoint fire retardant chemical drop on exact flame locations. We applied our method to a curated dataset derived from the FLAME2 dataset that includes RGB video along with IR video to determine the ground truth. Our proposed method has a unique property of detecting obscured fire and achieves a Dice score of 85.88%, while achieving a high precision of 92.47% and classification accuracy of 90.67% on test data showing promising results when inspected visually. Indeed, our method outperforms other methods by a significant margin in terms of video-level fire classification as we obtained about 100% accuracy using MobileNet+CBAM as the encoder backbone.

Related papers

• FlameFinder: Illuminating Obscured Fire through Smoke with Attentive Deep Metric Learning [11.218765140036254]
  FlameFinder is designed to accurately detect flames, even when obscured by smoke, using thermal images from firefighter drones during wildfire monitoring. Traditional RGB cameras struggle in such conditions, but thermal cameras can capture smoke-obscured flame features. To address this issue, FlameFinder utilizes paired thermal-RGB images for training. By learning latent flame features from smoke-free samples, the model becomes less biased towards relative thermal gradients.
  arXiv  Detail & Related papers  (2024-04-09T23:24:19Z)
• FSDNet-An efficient fire detection network for complex scenarios based on YOLOv3 and DenseNet [8.695064779659031]
  This paper proposes a fire detection network called FSDNet (Fire Smoke Detection Network), which consists of a feature extraction module, a fire classification module, and a fire detection module. The accuracy of FSDNet on the two benchmark datasets is 99.82% and 91.15%, respectively, and the average precision on MS-FS is 86.80%, which is better than the mainstream fire detection methods.
  arXiv  Detail & Related papers  (2023-04-15T15:46:08Z)
• 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)
• TempNet: Temporal Attention Towards the Detection of Animal Behaviour in Videos [63.85815474157357]
  We propose an efficient computer vision- and deep learning-based method for the detection of biological behaviours in videos. TempNet uses an encoder bridge and residual blocks to maintain model performance with a two-staged, spatial, then temporal, encoder. We demonstrate its application to the detection of sablefish (Anoplopoma fimbria) startle events.
  arXiv  Detail & Related papers  (2022-11-17T23:55:12Z)
• EAutoDet: Efficient Architecture Search for Object Detection [110.99532343155073]
  EAutoDet framework can discover practical backbone and FPN architectures for object detection in 1.4 GPU-days. We propose a kernel reusing technique by sharing the weights of candidate operations on one edge and consolidating them into one convolution. In particular, the discovered architectures surpass state-of-the-art object detection NAS methods and achieve 40.1 mAP with 120 FPS and 49.2 mAP with 41.3 FPS on COCO test-dev set.
  arXiv  Detail & Related papers  (2022-03-21T05:56:12Z)
• 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)
• Shot boundary detection method based on a new extensive dataset and mixed features [68.8204255655161]
  Shot boundary detection in video is one of the key stages of video data processing. New method for shot boundary detection based on several video features, such as color histograms and object boundaries, has been proposed.
  arXiv  Detail & Related papers  (2021-09-02T16:19:24Z)
• Active Fire Detection in Landsat-8 Imagery: a Large-Scale Dataset and a Deep-Learning Study [1.3764085113103217]
  This paper introduces a new large-scale dataset for active fire detection using deep learning techniques. We present a study on how different convolutional neural network architectures can be used to approximate handcrafted algorithms. The proposed dataset, source codes and trained models are available on Github.
  arXiv  Detail & Related papers  (2021-01-09T19:05:03Z)
• Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset [9.619617596045911]
  FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) offers a dataset of aerial images of fires. This paper provides a fire image dataset collected by drones during a prescribed burning piled detritus in an Arizona pine forest. The paper also highlights solutions to two machine learning problems: Binary classification of video frames based on the presence [and absence] of fire flames.
  arXiv  Detail & Related papers  (2020-12-28T00:00:41Z)
• Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction [61.17654438176999]
  We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design. Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
  arXiv  Detail & Related papers  (2020-11-05T11:34:12Z)
• Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection [12.515216618616206]
  We investigate different Convolutional Neural Network (CNN) architectures and their variants for the non-temporal real-time detection bounds of fire pixel regions in video (or still) imagery. Two reduced complexity compact CNN architectures (NasNet-A-OnFire and ShuffleNetV2-OnFire) are proposed through experimental analysis to optimise the computational efficiency for this task. We notably achieve a classification speed up by a factor of 2.3x for binary classification and 1.3x for superpixel localisation, with runtime of 40 fps and 18 fps respectively.
  arXiv  Detail & Related papers  (2020-10-17T17:48:04Z)

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.