Improved Active Fire Detection using Operational U-Nets

• URL: http://arxiv.org/abs/2304.09721v1
• Date: Wed, 19 Apr 2023 15:08:37 GMT
• Title: Improved Active Fire Detection using Operational U-Nets
• Authors: Ozer Can Devecioglu, Mete Ahishali, Fahad Sohrab, Turker Ince, Moncef Gabbouj
• Abstract summary: Using satellite imagery to monitor and detect active fires has been critical for managing forests and public land. We propose a novel approach called Operational U-Nets for the improved early detection of active fires.
• Score: 18.786429304405097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide. Warmer temperatures and drier conditions can cause quickly spreading fires and make them harder to control; therefore, early detection and accurate locating of active fires are crucial in environmental monitoring. Using satellite imagery to monitor and detect active fires has been critical for managing forests and public land. Many traditional statistical-based methods and more recent deep-learning techniques have been proposed for active fire detection. In this study, we propose a novel approach called Operational U-Nets for the improved early detection of active fires. The proposed approach utilizes Self-Organized Operational Neural Network (Self-ONN) layers in a compact U-Net architecture. The preliminary experimental results demonstrate that Operational U-Nets not only achieve superior detection performance but can also significantly reduce computational complexity.

Related papers

• Decision support system for Forest fire management using Ontology with Big Data and LLMs [0.8668211481067458]
  Fire weather indices, which assess wildfire risk and predict resource demands, are vital. With the rise of sensor networks in fields like healthcare and environmental monitoring, semantic sensor networks are increasingly used to gather climatic data. This paper discusses using Apache Spark for early forest fire detection, enhancing fire risk prediction with meteorological and geographical data.
  arXiv  Detail & Related papers  (2024-05-18T17:30:30Z)
• A Synergistic Approach to Wildfire Prevention and Management Using AI, ML, and 5G Technology in the United States [44.99833362998488]
  This research investigates proactive methods for detecting and handling wildfires in the United States. The specific objective of this research covers proactive detection and prevention of wildfires using advanced technology. Various methods, such as AI-enabled remote sensing and 5G-based active monitoring, can enhance proactive wildfire detection and management.
  arXiv  Detail & Related papers  (2024-02-27T04:09:30Z)
• A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management [6.043705525669726]
  Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife. Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management.
  arXiv  Detail & Related papers  (2024-01-04T05:09:35Z)
• Multimodal Wildland Fire Smoke Detection [5.15911752972989]
  Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the U.S. We present our work on integrating multiple data sources in SmokeyNet, a deep learning model usingtemporal information to detect smoke from wildland fires. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.
  arXiv  Detail & Related papers  (2022-12-29T01:16:06Z)
• 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)
• Label-Efficient Object Detection via Region Proposal Network Pre-Training [58.50615557874024]
  We propose a simple pretext task that provides an effective pre-training for the region proposal network (RPN) In comparison with multi-stage detectors without RPN pre-training, our approach is able to consistently improve downstream task performance.
  arXiv  Detail & Related papers  (2022-11-16T16:28:18Z)
• An Empirical Study of Adder Neural Networks for Object Detection [67.64041181937624]
  Adder neural networks (AdderNets) have shown impressive performance on image classification with only addition operations. We present an empirical study of AdderNets for object detection.
  arXiv  Detail & Related papers  (2021-12-27T11:03:13Z)
• 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)
• Bandit Quickest Changepoint Detection [55.855465482260165]
  Continuous monitoring of every sensor can be expensive due to resource constraints. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
  arXiv  Detail & Related papers  (2021-07-22T07:25:35Z)
• Unmanned Aerial Systems for Wildland and Forest Fires [0.0]
  Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Unmanned Aerial Systems (UAS) have proven to be useful due to their maneuverability.
  arXiv  Detail & Related papers  (2020-04-28T23:01:12Z)

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.