基于视觉的室内火灾和烟雾探测迁移学习方法的开发和评估

IF 1.5 4区 工程技术 Q3 CONSTRUCTION & BUILDING TECHNOLOGY
James Pincott, P. Tien, S. Wei, John Kaiser Calautit
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引用次数: 5

摘要

火灾在工业和家庭环境中构成了重大风险,尤其是对必须处理火灾的消防员来说。当前在室内环境中进行检测的技术是烟雾探测器和火焰探测器。然而,这些探测器在火灾和传播的点火阶段有几个局限性。这些系统无法检测火灾的确切位置,也无法检测火灾是如何蔓延或其规模的,所有这些都是消防部门在处理这些事件时所必需的信息。一个潜在的解决方案是使用计算机视觉等人工智能技术,该技术已显示出检测和识别室内空间中物体和活动的潜力。本研究旨在开发一种基于视觉的火灾和烟雾探测系统。结合卷积神经网络(CNN)的深度学习技术被用于开发实时检测方法,该方法可能为消防部门提供必要的信息,包括识别火灾的位置和规模以及火灾如何蔓延。使用预训练模型的迁移学习方法来训练检测器。基于室内火灾和烟雾视频的检测和识别测试,结果表明,火灾检测实现了高达92.37%的正确检测,而烟雾检测则表现不佳。因此,在未来的工作中,将对检测方法进行进一步的改进和评估,重点关注检测模型、建筑类型、室内空间大小和检测相机定位等不同参数的影响。本研究深入了解了这一概念的能力和潜在应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and evaluation of a vision-based transfer learning approach for indoor fire and smoke detection
Fire poses a significant risk across industrial and domestic settings, especially to firefighters who must tackle the blaze. Current technology for detection in indoor environments are smoke detectors and flame detectors. However, these detectors have several limitations during the ignition phase of a fire and propagation. These systems cannot detect an exact position of the fire nor how the fire is spreading or its size, all of which is necessary information for fire services when dealing with these incidents. A potential solution is to use artificial intelligence techniques such as computer vision, which has shown the potential to detect and recognise objects and activities in indoor spaces. This study aims to develop a vision-based fire and smoke detection system. A deep learning technique that incorporates convolutional neural networks (CNN) was utilised to develop the real-time detection approach that can potentially provide necessary information for fire services, including identifying the position and size of the fire and how the fire spreads. A transfer learning approach using a pre-trained model was used to train the detector. Based on the detection and recognition tests using indoor fire and smoke videos, results indicated that the fire detection achieved up to 92.37% correct detections while the smoke detection did not perform as well. Hence, further improvement and evaluation of the detection approach will be conducted in future work, focusing on the impact of different parameters such as the detection model, building type, indoor space size and positioning of the detection camera. The present study provides an insight into the capabilities and potential applications of the concept.
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来源期刊
Building Services Engineering Research & Technology
Building Services Engineering Research & Technology 工程技术-结构与建筑技术
CiteScore
4.30
自引率
5.90%
发文量
38
审稿时长
>12 weeks
期刊介绍: Building Services Engineering Research & Technology is one of the foremost, international peer reviewed journals that publishes the highest quality original research relevant to today’s Built Environment. Published in conjunction with CIBSE, this impressive journal reports on the latest research providing you with an invaluable guide to recent developments in the field.
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