Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (23): 1-11.DOI: 10.3778/j.issn.1002-8331.2206-0154

• Research Hotspots and Reviews • Previous Articles     Next Articles

Overview of Smoke and Fire Detection Algorithms Based on Deep Learning

ZHU Yuhua, SI Yiyi, LI Zhihui   

  1. 1.School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
    2.Key Laboratory of Grain Information Processing and Control, Ministry of Education(Henan University of Technology), Zhengzhou 450001, China
  • Online:2022-12-01 Published:2022-12-01



  1. 1.河南工业大学 信息科学与工程学院,郑州 450001
    2.粮食信息处理与控制教育部重点实验室(河南工业大学),郑州 450001

Abstract: Among various disasters, fire is one of the main disasters that most often and universally threaten public safety and social development. With the rapid development of economic construction and the increasing size of cities, the number of major fire hazards has increased dramatically. However, the widely used smoke sensor method of fire detection is vulnerable to factors such as distance, resulting in untimely detection. The introduction of video surveillance systems has provided new ideas to solve this problem. Traditional image processing algorithms based on video are earlier proposed methods, and the recent rapid development of machine vision and image processing technologies has resulted in a series of methods using deep learning techniques to automatically detect fires in video and images, which have very important practical applications in the field of fire safety. In order to comprehensively analyze the improvements and applications related to deep learning methods for fire detection, this paper first briefly introduces the fire detection process based on deep learning, and then focuses on a detailed comparative analysis of deep methods for fire detection in three granularities:classification, detection, and segmentation, and elaborates the relevant improvements taken by each class of algorithms for existing problems. Finally, the problems of fire detection at the present stage are summarized and future research directions are proposed.

Key words: deep learning, fire detection, object classification, object detection, object segmentation

摘要: 在各种灾害中,火灾是最经常、最普遍的威胁公众安全和社会发展的主要灾害之一。随着经济建设的迅猛发展,城市规模日趋扩大,重大火灾隐患急剧增加。然而,目前广泛使用的烟雾传感器探测火灾的方法,易受距离等因素影响,导致检测不及时。视频监控系统的引入为解决这一问题提供了新思路,基于视频的传统图像处理算法是较早提出的方法,最近机器视觉与图像处理技术快速发展,涌现出一系列使用深度学习技术来自动检测视频和图像中火灾的方法,在消防安全领域具有非常重要的实际应用价值。为了综合分析火灾检测的深度学习方法相关改进及应用,简要介绍了基于深度学习的火灾检测流程,重点从分类、检测、分割3个粒度对火灾检测的深度方法详细对比分析,阐述每类算法针对现有问题采取的相关改进。总结现阶段火灾检测存在的问题,并提出未来的研究方向。

关键词: 深度学习, 火灾检测, 目标分类, 目标检测, 目标分割