Quoc Dung Nguyen, Ngoc Dau Mai, Van Huan Nguyen, Vijay Kakani, Hakil Kim
{"title":"SynFAGnet:用于生成真实火灾图像的全自动生成网络","authors":"Quoc Dung Nguyen, Ngoc Dau Mai, Van Huan Nguyen, Vijay Kakani, Hakil Kim","doi":"10.1007/s10694-023-01540-2","DOIUrl":null,"url":null,"abstract":"<p>This paper proposes a fully automated generative network (“SynFAGnet”) for automatically creating a realistic-looking synthetic fire image. SynFAGnet is used as a data augmentation technique to create diverse data for training models, thereby solving problems related to real data acquisition and data imbalances. SynFAGnet comprises two main parts: an object-scene placement net (OSPNet) and a local–global context-based generative adversarial network (LGC-GAN). The OSPNet identifies suitable positions and scales for fires corresponding to the background scene. The LGC-GAN enhances the realistic appearance of synthetic fire images created by a given fire object-background scene pair by assembling effects such as halos and reflections in the surrounding area in the background scene. A comparative analysis shows that SynFAGnet achieves better outcomes than previous studies for both the Fréchet inception distance and learned perceptual image patch similarity evaluation metrics (values of 17.232 and 0.077, respectively). In addition, SynFAGnet is verified as a practically applicable data augmentation technique for training datasets, as it improves the detection and instance segmentation performance.</p>","PeriodicalId":558,"journal":{"name":"Fire Technology","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SynFAGnet: A Fully Automated Generative Network for Realistic Fire Image Generation\",\"authors\":\"Quoc Dung Nguyen, Ngoc Dau Mai, Van Huan Nguyen, Vijay Kakani, Hakil Kim\",\"doi\":\"10.1007/s10694-023-01540-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This paper proposes a fully automated generative network (“SynFAGnet”) for automatically creating a realistic-looking synthetic fire image. SynFAGnet is used as a data augmentation technique to create diverse data for training models, thereby solving problems related to real data acquisition and data imbalances. SynFAGnet comprises two main parts: an object-scene placement net (OSPNet) and a local–global context-based generative adversarial network (LGC-GAN). The OSPNet identifies suitable positions and scales for fires corresponding to the background scene. The LGC-GAN enhances the realistic appearance of synthetic fire images created by a given fire object-background scene pair by assembling effects such as halos and reflections in the surrounding area in the background scene. A comparative analysis shows that SynFAGnet achieves better outcomes than previous studies for both the Fréchet inception distance and learned perceptual image patch similarity evaluation metrics (values of 17.232 and 0.077, respectively). In addition, SynFAGnet is verified as a practically applicable data augmentation technique for training datasets, as it improves the detection and instance segmentation performance.</p>\",\"PeriodicalId\":558,\"journal\":{\"name\":\"Fire Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-02-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fire Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s10694-023-01540-2\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fire Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10694-023-01540-2","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
SynFAGnet: A Fully Automated Generative Network for Realistic Fire Image Generation
This paper proposes a fully automated generative network (“SynFAGnet”) for automatically creating a realistic-looking synthetic fire image. SynFAGnet is used as a data augmentation technique to create diverse data for training models, thereby solving problems related to real data acquisition and data imbalances. SynFAGnet comprises two main parts: an object-scene placement net (OSPNet) and a local–global context-based generative adversarial network (LGC-GAN). The OSPNet identifies suitable positions and scales for fires corresponding to the background scene. The LGC-GAN enhances the realistic appearance of synthetic fire images created by a given fire object-background scene pair by assembling effects such as halos and reflections in the surrounding area in the background scene. A comparative analysis shows that SynFAGnet achieves better outcomes than previous studies for both the Fréchet inception distance and learned perceptual image patch similarity evaluation metrics (values of 17.232 and 0.077, respectively). In addition, SynFAGnet is verified as a practically applicable data augmentation technique for training datasets, as it improves the detection and instance segmentation performance.
期刊介绍:
Fire Technology publishes original contributions, both theoretical and empirical, that contribute to the solution of problems in fire safety science and engineering. It is the leading journal in the field, publishing applied research dealing with the full range of actual and potential fire hazards facing humans and the environment. It covers the entire domain of fire safety science and engineering problems relevant in industrial, operational, cultural, and environmental applications, including modeling, testing, detection, suppression, human behavior, wildfires, structures, and risk analysis.
The aim of Fire Technology is to push forward the frontiers of knowledge and technology by encouraging interdisciplinary communication of significant technical developments in fire protection and subjects of scientific interest to the fire protection community at large.
It is published in conjunction with the National Fire Protection Association (NFPA) and the Society of Fire Protection Engineers (SFPE). The mission of NFPA is to help save lives and reduce loss with information, knowledge, and passion. The mission of SFPE is advancing the science and practice of fire protection engineering internationally.