skip to main content
10.1145/3688867.3690170acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
research-article

High Quality Fire Smoke Dataset: A Benchmark for Fire and Smoke Detection

Published: 28 October 2024 Publication History

Abstract

In this paper, we present the High Quality Fire Smoke Dataset(HQFSD), a new comprehensive fire and smoke dataset tailored for training and evaluating fire detection algorithms. It currently comprises 12,166 meticulously selected images sourced from over 250 real-fire video clips available on the Internet. These images encompass a wide range of fire scenarios, including diverse geographical and environmental conditions, varying lighting, background, burning objects, processes and states, and levels of occlusion. Each image is annotated with classification labels and bounding boxes. Based on the HQFSD, we systematically study and benchmark 14 representative deep learning methods. We also present the testing results to researchers utilizing the dataset and provide an overall analysis of these 14 baseline methods. Furthermore, we compare HQFSD with another fire dataset, demonstrating its robust generalization capability. It is anticipated that the HQFSD will significantly contribute to the advancement of fire detection research and applications.

References

[1]
AhmadA.A.Alkhatib.2014. AReviewonForestFireDetectionTechniques. InternationalJournalofDistributedSensorNetworks10,3(2014),597368. https: //doi.org/10.1155/2014/597368arXiv:https://doi.org/10.1155/2014/597368
[2]
AlexeyBochkovskiy,Chien-YaoWang,andHong-YuanMarkLiao.2020.YOLOv4: OptimalSpeedandAccuracyofObjectDetection.CoRRabs/2004.10934(2020). arXiv:2004.10934https://arxiv.org/abs/2004.10934
[3]
ZhaoweiCaiandNunoVasconcelos.2021.CascadeR-CNN:HighQualityObject DetectionandInstanceSegmentation. IEEETransactionsonPatternAnalysisand MachineIntelligence43,5(2021),1483--1498. https://doi.org/10.1109/TPAMI.2019. 2956516
[4]
NicolasCarion,FranciscoMassa,GabrielSynnaeve,NicolasUsunier,Alexan derKirillov,andSergeyZagoruyko.2020. End-to-EndObjectDetectionwith Transformers.InComputerVision--ECCV2020,AndreaVedaldi,HorstBischof, ThomasBrox,andJan-MichaelFrahm(Eds.).SpringerInternationalPublishing, Cham,213--229.
[5]
Turgay Celik, Hasan Demirel, Huseyin Ozkaramanli, and Mustafa Uyguroglu. 2007. Fire detection using statistical color model in video sequences. Journal of Visual Communication and Image Representation 18, 2 (2007), 176--185. https: //doi.org/10.1016/j.jvcir.2006.12.003
[6]
A. Enis Cetin. 2014. visFire. Retrieved February 28, 2008 from http://signal.ee. bilkent.edu.tr/VisiFire/
[7]
Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, et al. 2019. MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155 (2019).
[8]
Thou-Ho Chen, Ping-Hsueh Wu, and Yung-Chuen Chiou. 2004. An early fire detection method based on image processing. In 2004 International Conference on Image Processing, 2004. ICIP '04., Vol. 3. 1707--1710 Vol. 3. https://doi.org/10. 1109/ICIP.2004.1421401
[9]
Daniel YT Chino, Letricia PS Avalhais, Jose F Rodrigues, and Agma JM Traina. 2015. Bowfire: detection of fire in still images by integrating pixel color and texture analysis. In 2015 28th SIBGRAPIconference ongraphics, patterns andimages. IEEE, 95--102.
[10]
Pedro Vinícius AB de Venâncio, Adriano C Lisboa, and Adriano V Barbosa. 2022. Anautomatic fire detection system based on deep convolutional neural networks for low-power, resource-constrained devices. Neural Computing and Applications 34, 18 (2022), 15349--15368.
[11]
JiaDeng,WeiDong,RichardSocher,Li-JiaLi,KaiLi,andLiFei-Fei.2009. Imagenet: Alarge-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee, 248--255.
[12]
Dunnings and Andy. 2018. Fire Image Data Set for Dunnings 2018 Study- PNG Still Image Set. Retrieved May 27, 2019 from http://collections.durham.ac.uk/ f iles/r2d217qp536
[13]
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. 2010. The pascal visual object classes (voc) challenge. Inter national journal of computer vision 88 (2010), 303--338.
[14]
Pasquale Foggia, Alessia Saggese, and Mario Vento. 2015. Real-Time Fire Detec tion for Video-Surveillance Applications Using a Combination of Experts Based on Color, Shape, and Motion. IEEE Transactions on Circuits and Systems for Video Technology 25, 9 (2015), 1545--1556. https://doi.org/10.1109/TCSVT.2015.2392531
[15]
Anshul Gaur, Abhishek Singh, Ashok Kumar, Kishor S. Kulkarni, Sayantani Lala, Kamal Kapoor, Vishal Srivastava, Anuj Kumar, and Subhas Chandra Mukhopad hyay. 2019. Fire Sensing Technologies: A Review. IEEE Sensors Journal 19, 9 (2019), 3191--3202. https://doi.org/10.1109/JSEN.2019.2894665
[16]
Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, and Jian Sun. 2021. YOLOX: Exceeding YOLO Series in 2021. arXiv preprint arXiv:2107.08430 (2021).
[17]
gengyanlei. 2020. fire-smoke-detect-yolov4. Retrieved July 14, 2020 from https: //github.com/gengyanlei/fire-smoke-detect-yolov4
[18]
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le. 2019. Nas-fpn: Learning scalable feature pyramid architecture for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7036--7045.
[19]
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2015. Region based convolutional networks for accurate object detection and segmentation. IEEE transactions on pattern analysis and machine intelligence 38, 1 (2015), 142 158.
[20]
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. 2017. Accurate, large minibatch sgd: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677 (2017).
[21]
Nikos Grammalidis, Kosmas Dimitropoulos, and Enis Cetin. 2017. FIRESENSE database of videos for flame and smoke detection. https://doi.org/10.5281/zenodo. 836749
[22]
Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, Zhaohui Yang, Yiman Zhang, and Dacheng Tao. 2023. A Survey on Vision Transformer. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 1 (2023), 87--110. https://doi.org/10. 1109/TPAMI.2022.3152247
[23]
Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick. 2017. Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).
[24]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual LearningforImageRecognition.InProceedingsoftheIEEEConferenceonComputer Vision and Pattern Recognition (CVPR).
[25]
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. 2017. Densely Connected Convolutional Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26]
Valquíria Hüttner, Cristiano Rafael Steffens, and Silvia Silva da Costa Botelho. 2017. First response fire combat: Deep leaning based visible fire detection. In 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR). 1--6. https://doi.org/10.1109/SBR-LARS-R.2017.8215312
[27]
Glenn Jocher. 2020. YOLOv5 by Ultralytics. https://doi.org/10.5281/zenodo. 3908559
[28]
Glenn Jocher, Ayush Chaurasia, and Jing Qiu. 2023. Ultralytics YOLO. https: //github.com/ultralytics/ultralytics
[29]
Kang Kim and Hee Seok Lee. 2020. Probabilistic Anchor Assignment with IoU Prediction for Object Detection. In ECCV.
[30]
ByoungChul Ko, Joon-Young Kwak, and Jae-Yeal Nam. 2012. Wildfire smoke detection using temporospatial features and random forest classifiers. Optical Engineering 51, 1 (2012), 017208--017208.
[31]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. ImageNet Clas sification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, F. Pereira, C.J. Burges, L. Bottou, and K.Q. Wein berger (Eds.), Vol. 25. Curran Associates, Inc. https://proceedings.neurips.cc/ paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
[32]
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, et al. 2020. The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale. International journal of computer vision 128, 7 (2020), 1956--1981.
[33]
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436--444.
[34]
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278--2324. https: //doi.org/10.1109/5.726791
[35]
Chuyi Li, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng, Liang Li, Zaidan Ke, Qingyuan Li, Meng Cheng, Weiqiang Nie, et al. 2022. YOLOv6: A single stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976 (2022).
[36]
Yuming Li, Wei Zhang, Yanyan Liu, and Yao Jin. 2022. A visualized fire detec tion method based on convolutional neural network beyond anchor. Applied Intelligence 52, 11 (2022), 13280--13295.
[37]
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. 2017. Focal Loss for Dense Object Detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).
[38]
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In Computer Vision--ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6--12, 2014, Proceedings, Part V 13. Springer, 740 755.
[39]
Che-Bin Liu and N. Ahuja. 2004. Vision based fire detection. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., Vol. 4. 134--137 Vol.4. https://doi.org/10.1109/ICPR.2004.1333722
[40]
Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, and Lei Zhang. 2022. Dab-detr: Dynamic anchor boxes are better queries for detr. arXiv preprint arXiv:2201.12329 (2022).
[41]
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. 2016. SSD: Single Shot MultiBox Detector. In Computer Vision-- ECCV 2016, Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling (Eds.). Springer International Publishing, Cham, 21--37.
[42]
Jingrun Ma, Zhengwei Zhang, Weien Xiao, Xinlei Zhang, and Shaozhang Xiao. 2023. Flame and Smoke Detection Algorithm Based on ODConvBS-YOLOv5s. IEEE Access 11 (2023), 34005--34014. https://doi.org/10.1109/ACCESS.2023. 3263479
[43]
Siyuan Qiao, Liang-Chieh Chen, and Alan Yuille. 2020. DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution. arXiv preprint arXiv:2006.02334 (2020).
[44]
Yue-Yan Qin, Jiang-Tao Cao, and Xiao-Fei Ji. 2021. Fire detection method based on depthwise separable convolution and yolov3. International Journal of Automation and Computing 18, 2 (2021), 300--310.
[45]
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46]
Joseph Redmon and Ali Farhadi. 2017. YOLO9000: Better, Faster, Stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47]
Joseph Redmon and Ali Farhadi. 2018. YOLOv3: An Incremental Improvement. CoRR abs/1804.02767 (2018). arXiv:1804.02767 http://arxiv.org/abs/1804.02767
[48]
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015).
[49]
Sergio Saponara, Abdussalam Elhanashi, and Alessio Gagliardi. 2021. Real-time video fire/smoke detection based on CNNinantifiresurveillance systems. Journal of Real-Time Image Processing 18 (2021), 889--900.
[50]
Alireza Shamsoshoara, Fatemeh Afghah, Abolfazl Razi, Liming Zheng, Peter Z Fulé, and Erik Blasch. 2021. Aerial imagery pile burn detection using deep learning: The FLAME dataset. Computer Networks 193 (2021), 108001.
[51]
Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Net works for Large-Scale Image Recognition. arXiv:1409.1556 [cs.CV] https: //arxiv.org/abs/1409.1556
[52]
Yu Sun and Jian Feng. 2023. Fire and smoke precise detection method based on the attention mechanism and anchor-free mechanism. Complex & Intelligent Systems 9, 5 (2023), 5185--5198.
[53]
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going Deeper With Convolutions. In Proceedings of the IEEE Conference on Com puter Vision and Pattern Recognition (CVPR).
[54]
Tom Toulouse, Lucile Rossi, Antoine Campana, Turgay Celik, and Moulay A Akhloufi. 2017. Computer vision for wildfire research: An evolving image dataset for processing and analysis. Fire Safety Journal 92 (2017), 188--194.
[55]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, 'ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).
[56]
Chien-Yao Wang,AlexeyBochkovskiy, and Hong-YuanMarkLiao.2023. YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detec tors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 7464--7475.
[57]
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. 2018. CBAM: Convolutional Block Attention Module. In Proceedings of the European Conference on Computer Vision (ECCV).
[58]
Siyuan Wu, Xinrong Zhang, Ruqi Liu, and Binhai Li. 2023. A dataset for fire and smoke object detection. Multimedia Tools and Applications 82, 5 (2023), 6707--6726.
[59]
Yue Wu, Yinpeng Chen, Lu Yuan, Zicheng Liu, Lijuan Wang, Hongzhi Li, and Yun Fu. 2020. Rethinking Classification and Localization for Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
[60]
Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, and Heung-Yeung Shum. 2022. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605 (2022).
[61]
Jianmei Zhang, Hongqing Zhu, Pengyu Wang, and Xiaofeng Ling. 2021. ATT Squeeze U-Net: ALightweightNetworkforForestFireDetection andRecognition. IEEE Access 9 (2021), 10858--10870. https://doi.org/10.1109/ACCESS.2021.3050628
[62]
Shilong Zhang, Xinjiang Wang, Jiaqi Wang, Jiangmiao Pang, Chengqi Lyu, Wen wei Zhang, Ping Luo, and Kai Chen. 2023. Dense Distinct Query for End-to-End Object Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 7329--7338.
[63]
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. 2020. Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020).
[64]
Zhengxia Zou, Keyan Chen, Zhenwei Shi, Yuhong Guo, and Jieping Ye. 2023. Object detection in 20 years: A survey. Proc. IEEE 111, 3 (2023), 257--276

Index Terms

  1. High Quality Fire Smoke Dataset: A Benchmark for Fire and Smoke Detection

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    McGE '24: Proceedings of the 2nd International Workshop on Multimedia Content Generation and Evaluation: New Methods and Practice
    October 2024
    77 pages
    ISBN:9798400711947
    DOI:10.1145/3688867
    • Program Chairs:
    • Cheng Jin,
    • Liang He,
    • Mingli Song,
    • Rui Wang
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 28 October 2024

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. deep learning
    2. fire and smoke dataset
    3. fire and smoke detection

    Qualifiers

    • Research-article

    Conference

    MM '24
    Sponsor:
    MM '24: The 32nd ACM International Conference on Multimedia
    October 28 - November 1, 2024
    Melbourne VIC, Australia

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 124
      Total Downloads
    • Downloads (Last 12 months)124
    • Downloads (Last 6 weeks)20
    Reflects downloads up to 15 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media