Computer vision-based characterization of large-scale jet flames using a synthetic infrared image generation approach
Authors: 
Carmina Pérez-Guerrero, Jorge Francisco Ciprián-Sánchez, Adriana Palacios, Gilberto Ochoa-Ruiz, Miguel Gonzalez-Mendoza, Vahid Foroughi, Elsa Pastor, Gerardo Rodriguez-HernandezAuthors Info & Claims
Carmina Pérez-Guerrero, Jorge Francisco Ciprián-Sánchez, Adriana Palacios, Gilberto Ochoa-Ruiz, Miguel Gonzalez-Mendoza, Vahid Foroughi, Elsa Pastor, Gerardo Rodriguez-HernandezAuthors Info & ClaimsReferences
[1]
A. Croce P., Mudan K.S., Calculating impacts for large open hydrocarbon fires, Fire Saf. J. 11 (1) (1986) 99–112,.
[2]
Badrinarayanan V., Kendall A., Cipolla R., SegNet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 39 (12) (2017) 2481–2495,.
[3]
Bradski G., The OpenCV library, Dr. Dobb’s J. Softw. Tools (2000).
[4]
Casal J., Chapter 10 - Domino effect, in: Evaluation of the Effects and Consequences of Major Accidents in Industrial Plants, second ed., Elsevier, 2018, pp. 405–437,.
[5]
Chamberlain G.A., Developments in design methods for predicting thermal radiation from flares, Chem. Eng. Res. Des. 65 (4) (1987).
[6]
Chen L., Papandreou G., Kokkinos I., Murphy K., Yuille A.L., DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE Trans. Pattern Anal. Mach. Intell. 40 (4) (2018) 834–848,.
[7]
Cheon M., Vigier T., Krasula L., Lee J., Le Callet P., Lee J.-S., Ambiguity of objective image quality metrics: A new methodology for performance evaluation, Signal Process., Image Commun. 93 (2021),. URL: https://www.sciencedirect.com/science/article/pii/S0923596521000096.
[8]
Ciprián-Sánchez J.F., Ochoa-Ruiz G., Gonzalez-Mendoza M., Rossi L., FIRe-GAN: A novel deep learning-based infrared-visible fusion method for wildfire imagery, Neural Comput. Appl. (2021),.
[9]
Colella F., Ibarreta A., Hart R.J., Morrison T., Watson H.A., Yen M., Jet fire consequence analysis, in: OTC Offshore Technology Conference, 2020,.
[10]
Fay J., Model of large pool fires, J. Hazardous Mater. 136 2 (2006) 219–232.
[11]
Gong F., Li C., Gong W., Li X., Yuan X., Ma Y., Song T., A real-time fire detection method from video with multifeature fusion, Comput. Intell. Neurosci. 2019 (2019),.
[12]
Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y., Generative adversarial nets, in: Ghahramani Z., Welling M., Cortes C., Lawrence N., Weinberger K.Q. (Eds.), Advances in Neural Information Processing Systems. Vol. 27, Curran Associates, Inc, 2014, pp. 2672–2680. URL: https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf.
[13]
Gu K., Zhang Y., Qiao J., Vision-based monitoring of flare soot, IEEE Trans. Instrum. Meas. 69 (9) (2020) 7136–7145,.
[14]
Guiberti T., Boyette W., Roberts W., Height of turbulent non-premixed jet flames at elevated pressure, Combust. Flame 220 (2020) 407–409,.
[15]
Iandola F.N., Moskewicz M.W., Ashraf K., Han S., Dally W.J., Keutzer K., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size, 2016, CoRR, arXiv:1602.07360.
[16]
Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A., 2017. Image-to-Image Translation with Conditional Adversarial Networks. In: CVPR.
[17]
Janssen R., Sepasian N., Automatic flare-stack monitoring, SPE Prod. Oper. 34 (01) (2018) 18–23,.
[18]
Kashi E., Bahoosh M., Jet fire assessment in complex environments using computational fluid dynamics, Braz. J. Chem. Eng. 37 (2020) 203–212,.
[19]
Keshavarz G., Khan F., Hawboldt K., Modeling of pool fires in cold regions, Fire Saf. J. 48 (2012) 1–10,.
[20]
Klanderman G., Rucklidge W., Huttenlocher D., Comparing images using the Hausdorff distance, IEEE Trans. Pattern Anal. Mach. Intell. 15 (09) (1993) 850–863,.
[21]
Korhonen J., You J., Peak signal-to-noise ratio revisited: Is simple beautiful?, in: 2012 Fourth International Workshop on Quality of Multimedia Experience, 2012, pp. 37–38,.
[22]
Lattimer B., Hodges J., Lattimer A., Using machine learning in physics-based simulation of fire, Fire Saf. J. 114 (2020),.
[23]
Litjens G., Kooi T., Bejnordi B.E., Setio A.A.A., Ciompi F., Ghafoorian M., van der Laak J.A., van Ginneken B., Sánchez C.I., A survey on deep learning in medical image analysis, Med. Image Anal. 42 (2017) 60–88,.
[24]
Mahony N.O., Campbell S., Carvalho A., Harapanahalli S., Velasco-Hernández G.A., Krpalkova L., Riordan D., Walsh J., Deep learning vs. Traditional computer vision, Adv. Intell. Syst. Comput. 943 (2019) 128–144,.
[25]
Mao J., Zheng C., Yin J., Tian Y., Cui W., Wildfire smoke classification based on synthetic images and pixel- and feature-level domain adaptation, Sensors 21 (23) (2021),. URL: https://www.mdpi.com/1424-8220/21/23/7785.
[26]
Mashhadimoslem H., Ghaemi A., Palacios A., Analysis of deep learning neural network combined with experiments to develop predictive models for a propane vertical jet fire, Heliyon 6 (11) (2020),.
[27]
Milz, S., Rudiger, T., Suss, S., 2018. Aerial GANeration: Towards Realistic Data Augmentation Using Conditional GANs. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops.
[28]
Mirza M., Osindero S., Conditional generative adversarial nets, 2014, arXiv:1411.1784.
[29]
Official Journal of the European Community M., EU : Council Directive 82/501EEC of 24 June 1982 on the major accident hazards of certain industrial activities, 1982, p. 1. OJ L 230 05.08.1982.
[30]
Official Journal of the European Community M., EU : Council Directive 96/82/EC of 9 December 1996 on the control of major-accident hazards involving dangerous substances, 1996, pp. 13–33. OJ L 10 14.1.1997.
[31]
Official Journal of the European Community M., EU : Directive 2003/105/EC of the European Parliament and of the Council of 16 December 2003 amending Council Directive 96/82/EC on the control of major-accident hazards involving dangerous substances, 2003, pp. 97–105. OJ L 345 31.12.2003.
[32]
Oktay O., Schlemper J., Folgoc L.L., Lee M., Heinrich M., Misawa K., Mori K., McDonagh S., Hammerla N.Y., Kainz B., Glocker B., Rueckert D., Attention U-Net: Learning where to look for the pancreas, 2018, arXiv:1804.03999.
[33]
Palacios A., Study of Jet Fires Geometry and Radiative Features, (Ph.D. thesis) Universitat Politècnica de Catalunya, 2011.
[34]
Palacios A., Muñoz M., Darbra R., Casal J., Thermal radiation from vertical jet fires, Fire Saf. J. 51 (2012) 93–101,. URL: https://www.sciencedirect.com/science/article/pii/S0379711212000495.
[35]
Pan Z., Yu W., Yi X., Khan A., Yuan F., Zheng Y., Recent progress on generative adversarial networks (GANs): A survey, IEEE Access 7 (2019) 36322–36333,.
[36]
Park M., Tran D.Q., Jung D., Park S., Wildfire-detection method using DenseNet and CycleGAN data augmentation-based remote camera imagery, Remote Sens. 12 (22) (2020),. URL: https://www.mdpi.com/2072-4292/12/22/3715.
[37]
Paszke A., Chaurasia A., Kim S., Culurciello E., ENet: A deep neural network architecture for real-time semantic segmentation, 2016, arXiv.
[38]
Paszke A., Gross S., Massa F., Lerer A., Bradbury J., Chanan G., Killeen T., Lin Z., Gimelshein N., Antiga L., Desmaison A., Kopf A., Yang E., DeVito Z., Raison M., Tejani A., Chilamkurthy S., Steiner B., Fang L., Bai J., Chintala S., PyTorch: An imperative style, high-performance deep learning library, in: Wallach H., Larochelle H., Beygelzimer A., d’Alché-Buc F., Fox E., Garnett R. (Eds.), Advances in Neural Information Processing Systems. Vol. 32, Curran Associates, Inc, 2019, pp. 8024–8035.
[39]
Pérez-Guerrero C., Palacios A., Ochoa-Ruiz G., Foroughi V., Pastor E., Gonzalez-Mendoza M., Falcón-Morales L.E., Experimental large-scale jet flames’ geometrical features extraction for risk management using infrared images and deep learning segmentation methods, J. Loss Prev. Process Ind. 80 (2022),. URL: https://www.sciencedirect.com/science/article/pii/S0950423022001796.
[40]
Pérez-Guerrero C., Palacios A., Ochoa-Ruiz G., Mata C., Gonzalez-Mendoza M., Falcón-Morales L.E., Comparing machine learning based segmentation models on jet fire radiation zones, in: Batyrshin I., Gelbukh A., Sidorov G. (Eds.), Advances in Computational Intelligence, Springer International Publishing, Cham, 2021, pp. 161–172,.
[41]
Preedanan W., Kondo T., Bunnun P., Kumazawa I., A comparative study of image quality assessment, in: 2018 International Workshop on Advanced Image Technology, IWAIT, 2018, pp. 1–4,.
[42]
Radford A., Metz L., Chintala S., Unsupervised representation learning with deep convolutional generative adversarial networks, 2016, arXiv:1511.06434.
[43]
Roberts J.W., van Aardt J.A., Ahmed F.B., Assessment of image fusion procedures using entropy, image quality, and multispectral classification, J. Appl. Remote Sens. 2 (1) (2008) 1–28,.
[44]
Ronneberger O., Fischer P., Brox T., U-Net: Convolutional networks for biomedical image segmentation, Med. Image Comput. Comput.-Assist. Intervent. 9351 (2015) 234–241,.
[45]
Shannon C.E., A mathematical theory of communication, Bell Syst. Tech. J. 27 (3) (1948) 379–423,.
[46]
Shcherbakov M., Brebels A., Shcherbakova N., Tyukov A., Janovsky T., Kamaev V., A survey of forecast error measures, World Appl. Sci. J. 24 (2013) 171–176,.
[47]
Sheng P., Yang Z., Qian Y., GANs for children: A generative data augmentation strategy for children speech recognition, in: 2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU, 2019, pp. 129–135,.
[48]
Taha A.A., Hanbury A., Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool, BMC Med. Imag. 15 (29) (2015) 15–29,.
[49]
U.S. Chemical Safety and Hazard Investigation Board, 2008. LPG Fire at Valero – McKee Rrefinery. CSB Report No. 2007-05-I-TX,.
[50]
Wang Z., Zhou K., Zhang L., Nie X., Wu Y., Jiang J., Dederichs A.S., He L., Flame extension area and temperature profile of horizontal jet fire impinging on a vertical plate, Process Saf. Environ. Prot. 147 (2021) 547–558,.
[51]
Wu E., Wu K., Cox D., Lotter W., Conditional infilling GANs for data augmentation in Mammogram classification, in: Stoyanov D., Taylor Z., Kainz B., Maicas G., Beichel R.R., Martel A., Maier-Hein L., Bhatia K., Vercauteren T., Oktay O., Carneiro G., Bradley A.P., Nascimento J., Min H., Brown M.S., Jacobs C., Lassen-Schmidt B., Mori K., Petersen J., San José Estépar R., Schmidt-Richberg A., Veiga C. (Eds.), Image Analysis for Moving Organ, Breast, and Thoracic Images, Springer International Publishing, Cham, 2018, pp. 98–106.
[52]
Xu H., Ma J., Jiang J., Guo X., Ling H., U2Fusion: A unified unsupervised image fusion network, IEEE Trans. Pattern Anal. Mach. Intell. 44 (1) (2022) 502–518,.
[53]
Yang Z., Wang T., Bu L., Ouyang J., Training with augmented data: GAN-based flame-burning image synthesis for fire segmentation in warehouse, Fire Technol. 58 (1) (2022) 183–215,.
[54]
Zhang J., Zhu H., Wang P., Ling X., ATT squeeze U-Net: A lightweight network for forest fire detection and recognition, IEEE Access 9 (2021) 10858–10870,.
[55]
Zhao Y., Fu G., Wang H., Zhang S., The fusion of unmatched infrared and visible images based on generative adversarial networks, Math. Probl. Eng. 2020 (2020),.
[56]
Zhikai Y., Leping B., Teng W., Tianrui Z., Fen W., Fire image generation based on ACGAN, in: 2019 Chinese Control and Decision Conference, CCDC, 2019, pp. 5743–5746,.
[57]
Zhou Z., Siddiquee M.M.R., Tajbakhsh N., Liang J., UNet++: Redesigning skip connections to exploit multiscale features in image segmentation, IEEE Trans. Med. Imaging (2019),.
[58]
Zhou Wang Z., Bovik A.C., Sheikh H.R., Simoncelli E.P., Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process. 13 (4) (2004) 600–612.
[59]
Zhou Wang Z., Bovik A.C., Sheikh H.R., Simoncelli E.P., The SSIM index for image quality assessment, 2020, https://www.cns.nyu.edu/~lcv/ssim/. Accessed: 2020-08-26.
[60]
Zhu J., Li W., Lin D., Cheng H., Zhao G., Intelligent fire monitor for fire robot based on infrared image feedback control, Fire Technol. 56 (5) (2020) 2089–2109,.
Index Terms
- Computer vision-based characterization of large-scale jet flames using a synthetic infrared image generation approach
Index terms have been assigned to the content through auto-classification.
Recommendations
Interferometric Visualization of Jet Flames
This paper presents visualizations of reacting, round jets of the premixed and nonpremixed type realized by using interferometry and, complementarily, direct photography. The available interferometer, proposed by Carlomagno (1986), employs low-cost ...
Stylized Adversarial AutoEncoder for Image Generation
MM '17: Proceedings of the 25th ACM international conference on MultimediaIn this paper, we propose an autoencoder-based generative adversarial network (GAN) for automatic image generation, which is called "stylized adversarial autoencoder". Different from existing generative autoencoders which typically impose a prior ...
Comments
Information & Contributors
Information
Published In
Elsevier Ltd.
Publisher
Pergamon Press, Inc.
United States
Publication History
Published: 01 February 2024
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 10 Nov 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in