基于深度学习的视频火灾探测方法:数据集、方法和未来方向

IF 3 3区 农林科学 Q2 ECOLOGY Fire-Switzerland Pub Date : 2023-08-14 DOI:10.3390/fire6080315
Chengtuo Jin, Tao Wang, Naji Alhusaini, Shenghui Zhao, Huilin Liu, Kun Xu, Jin Zhang
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引用次数: 0

摘要

在各种灾难中,火灾是最普遍、最具威胁性的灾难之一,对公共安全和社会进步构成重大危险。传统的火灾探测系统主要依赖于基于传感器的探测技术,这在准确、及时地探测火灾方面具有固有的局限性,尤其是在复杂环境中。近年来,随着计算机视觉技术的进步,面向视频的火灾探测技术由于其非接触式传感、对不同环境的适应性和全面的信息采集,逐渐成为一种新的解决方案。然而,基于手工特征提取的方法难以应对由不同可燃物、照明条件和其他因素引起的烟雾或火焰变化。深度学习作为一种强大而灵活的机器学习框架,在视频火灾检测中显示出显著的优势。本文总结了基于深度学习的视频火灾检测方法,重点介绍了用于火灾识别、火灾目标检测和火灾分割的深度学习方法和常用数据集的最新进展。此外,本文还对该领域的发展前景进行了回顾和展望。
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Video Fire Detection Methods Based on Deep Learning: Datasets, Methods, and Future Directions
Among various calamities, conflagrations stand out as one of the most-prevalent and -menacing adversities, posing significant perils to public safety and societal progress. Traditional fire-detection systems primarily rely on sensor-based detection techniques, which have inherent limitations in accurately and promptly detecting fires, especially in complex environments. In recent years, with the advancement of computer vision technology, video-oriented fire detection techniques, owing to their non-contact sensing, adaptability to diverse environments, and comprehensive information acquisition, have progressively emerged as a novel solution. However, approaches based on handcrafted feature extraction struggle to cope with variations in smoke or flame caused by different combustibles, lighting conditions, and other factors. As a powerful and flexible machine learning framework, deep learning has demonstrated significant advantages in video fire detection. This paper summarizes deep-learning-based video-fire-detection methods, focusing on recent advances in deep learning approaches and commonly used datasets for fire recognition, fire object detection, and fire segmentation. Furthermore, this paper provides a review and outlook on the development prospects of this field.
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来源期刊
Fire-Switzerland
Fire-Switzerland Multiple-
CiteScore
3.10
自引率
15.60%
发文量
182
审稿时长
11 weeks
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