Chengtuo Jin, Tao Wang, Naji Alhusaini, Shenghui Zhao, Huilin Liu, Kun Xu, Jin Zhang
{"title":"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","authors":"Chengtuo Jin, Tao Wang, Naji Alhusaini, Shenghui Zhao, Huilin Liu, Kun Xu, Jin Zhang","doi":"10.3390/fire6080315","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":36395,"journal":{"name":"Fire-Switzerland","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Video Fire Detection Methods Based on Deep Learning: Datasets, Methods, and Future Directions\",\"authors\":\"Chengtuo Jin, Tao Wang, Naji Alhusaini, Shenghui Zhao, Huilin Liu, Kun Xu, Jin Zhang\",\"doi\":\"10.3390/fire6080315\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":36395,\"journal\":{\"name\":\"Fire-Switzerland\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fire-Switzerland\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.3390/fire6080315\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fire-Switzerland","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.3390/fire6080315","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECOLOGY","Score":null,"Total":0}
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.