Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments

• URL: http://arxiv.org/abs/2107.11043v1
• Date: Fri, 23 Jul 2021 06:35:13 GMT
• Title: Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments
• Authors: Manish Bhattarai
• Abstract summary: We present a new four-pronged approach to build firefighter's situational awareness for the first time in the literature. First, we used a deep Convolutional Neural Network (CNN) system to classify and identify objects of interest from thermal imagery in real-time. Next, we extended this CNN framework for object detection, tracking, segmentation with a Mask RCNN framework, and scene description with a multimodal natural language processing(NLP) framework. Third, we built a deep Q-learning-based agent, immune to stress-induced disorientation and anxiety, capable of making clear navigation decisions based on the observed
• Score: 4.061135251278187
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present a new four-pronged approach to build firefighter's situational awareness for the first time in the literature. We construct a series of deep learning frameworks built on top of one another to enhance the safety, efficiency, and successful completion of rescue missions conducted by firefighters in emergency first response settings. First, we used a deep Convolutional Neural Network (CNN) system to classify and identify objects of interest from thermal imagery in real-time. Next, we extended this CNN framework for object detection, tracking, segmentation with a Mask RCNN framework, and scene description with a multimodal natural language processing(NLP) framework. Third, we built a deep Q-learning-based agent, immune to stress-induced disorientation and anxiety, capable of making clear navigation decisions based on the observed and stored facts in live-fire environments. Finally, we used a low computational unsupervised learning technique called tensor decomposition to perform meaningful feature extraction for anomaly detection in real-time. With these ad-hoc deep learning structures, we built the artificial intelligence system's backbone for firefighters' situational awareness. To bring the designed system into usage by firefighters, we designed a physical structure where the processed results are used as inputs in the creation of an augmented reality capable of advising firefighters of their location and key features around them, which are vital to the rescue operation at hand, as well as a path planning feature that acts as a virtual guide to assist disoriented first responders in getting back to safety. When combined, these four approaches present a novel approach to information understanding, transfer, and synthesis that could dramatically improve firefighter response and efficacy and reduce life loss.

Related papers

• Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition [53.359383163184425]
  We introduce a novel multimodality synergistic knowledge distillation scheme tailored for efficient single-eye motion recognition tasks. This method allows a lightweight, unimodal student spiking neural network (SNN) to extract rich knowledge from an event-frame multimodal teacher network.
  arXiv  Detail & Related papers  (2024-06-20T07:24:47Z)
• Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement [2.4594411098435023]
  We propose using artificial intelligence techniques, specifically Deep Reinforcement Learning, to address the problem of firebreak placement in the landscape. We have successfully implemented a computational agent capable of learning firebreak locations within a forest environment, achieving good results. Our approach demonstrates convergence with highly favorable results in problem instances as large as 40 x 40 cells, marking a significant milestone in applying Reinforcement Learning to this critical issue.
  arXiv  Detail & Related papers  (2024-04-12T15:10:57Z)
• AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System [52.93806509364342]
  This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. We introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features. We incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency.
  arXiv  Detail & Related papers  (2023-12-18T12:37:35Z)
• Planning for Learning Object Properties [117.27898922118946]
  We formalize the problem of automatically training a neural network to recognize object properties as a symbolic planning problem. We use planning techniques to produce a strategy for automating the training dataset creation and the learning process. We provide an experimental evaluation in both a simulated and a real environment.
  arXiv  Detail & Related papers  (2023-01-15T09:37:55Z)
• Backprop-Free Reinforcement Learning with Active Neural Generative Coding [84.11376568625353]
  We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference. The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
  arXiv  Detail & Related papers  (2021-07-10T19:02:27Z)
• A deep Q-Learning based Path Planning and Navigation System for Firefighting Environments [3.24890820102255]
  We propose a deep Q-learning based agent who is immune to stress induced disorientation and anxiety. As a proof of concept, we imitate structural fire in a gaming engine called Unreal Engine. We exploit experience replay to accelerate the learning process and augment the learning of the agent with human-derived experiences.
  arXiv  Detail & Related papers  (2020-11-12T15:43:17Z)
• An embedded deep learning system for augmented reality in firefighting applications [2.750124853532832]
  This research implements recent advancements in technology such as deep learning, point cloud and thermal imaging, and augmented reality platforms. We have designed and built a prototype embedded system that can leverage data streamed from cameras built into a firefighter's personal protective equipment (PPE) to capture thermal, RGB color, and depth imagery. The embedded system analyzes and returns the processed images via wireless streaming, where they can be viewed remotely and relayed back to the firefighter using an augmented reality platform.
  arXiv  Detail & Related papers  (2020-09-22T16:55:44Z)
• Distilled Semantics for Comprehensive Scene Understanding from Videos [53.49501208503774]
  In this paper, we take an additional step toward holistic scene understanding with monocular cameras by learning depth and motion alongside with semantics. We address the three tasks jointly by a novel training protocol based on knowledge distillation and self-supervision. We show that it yields state-of-the-art results for monocular depth estimation, optical flow and motion segmentation.
  arXiv  Detail & Related papers  (2020-03-31T08:52:13Z)
• Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D Environments [11.657524999491029]
  In this work, we used deep reinforcement learning combining Q-learning with a neural representation to avoid instability. Our methodology uses deep q-learning and combines it with a rolling wave planning approach on agile methodology. Experimental results show that the proposed method enhanced the performance of VVN by 55.31 on average for long-distance missions.
  arXiv  Detail & Related papers  (2020-03-23T12:58:58Z)
• On Simple Reactive Neural Networks for Behaviour-Based Reinforcement Learning [5.482532589225552]
  We present a behaviour-based reinforcement learning approach, inspired by Brook's subsumption architecture. Our working assumption is that a pick and place robotic task can be simplified by leveraging domain knowledge of a robotics developer. Our approach learns the pick and place task in 8,000 episodes, which represents a drastic reduction in the number of training episodes required by an end-to-end approach.
  arXiv  Detail & Related papers  (2020-01-22T11:49:52Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.