Browse > Article
http://dx.doi.org/10.5762/KAIS.2020.21.12.320

Development of Fire Detection Model for Underground Utility Facilities Using Deep Learning : Training Data Supplement and Bias Optimization  

Kim, Jeongsoo (Korea BIM Research Center, Korea Institute of Civil Engineering and Building Technologies)
Lee, Chan-Woo (SPIN A WEB)
Park, Seung-Hwa (Korea BIM Research Center, Korea Institute of Civil Engineering and Building Technologies)
Lee, Jong-Hyun (SPIN A WEB)
Hong, Chang-Hee (Korea BIM Research Center, Korea Institute of Civil Engineering and Building Technologies)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.12, 2020 , pp. 320-330 More about this Journal
Abstract
Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.
Keywords
Underground Utility Facility; Fire Detection; Deep Learning; Convolutional Neural Network; Bias Training;
Citations & Related Records
Times Cited By KSCI : 12  (Citation Analysis)
연도 인용수 순위
1 J. Choi, "Development of Estimation Model for Hysteresis of Friction Using Artificial Intelligent," Journal of the Korea Academia-Industrial Cooperation Society, vol.12, no.7, pp.2913-2918, July 2011. (In Korean) DOI: https://doi.org/10.5762/KAIS.2011.12.7.2913   DOI
2 B. Lee, D. Han, "Flame and Smoke Detection Method for Early and Real-time Detection of Tunnel Fire," The Institute of Electronics Engineers of Korea-Signal Processing, vol.45, no.4, pp.59-70, July 2008. (In Korean)
3 J. Yim, H. Park, W. Lee, M. S. Kim, Y. T. Lee, "Deep Learning Based CCTV Fire Detection System," Proceeding of the Korea Institute of Broadcast and Media Engineers, pp.139-141, 2017. (In Korean)
4 H. S. Shin, K. B. Lee, M. J. Yim, D. G. Kim, "Development of a Deep-Learning Based Tunnel Incident Detection System on CCTVs," Journal of Korean Tunnelling and Underground Space Association, vol.19, no.6, pp.915-936, 2017. (In Korean) DOI: https://doi.org/10.9711/KTAJ.2017.19.6.915   DOI
5 H. S. Shin, D. G. Kim, M. J. Yim, K. B. Lee, , Y. S. Oh, "A Preliminary Study for Development of an Automatic Incident Detection System on CCTV in Tunnels Based on a Machine Learning Algorithm," Journal of Korean Tunnelling and Underground Space Association, vol.19, no.1, pp.95-107, 2017. (In Korean) DOI: https://doi.org/10.9711/KTAJ.2017.19.1.095   DOI
6 K. B. Lee, H. S. Shin, "Effect on Self-Enhancement of Deep-Learning Inference by Repeated Training of False Detection Cases in Tunnel Accident Image Detection," Journal of Korean Tunnelling and Underground Space Association, vol.21, no.3, pp.419-432, 2019. (In Korean) DOI: https://doi.org/10.9711/KTAJ.2019.21.3.419   DOI
7 T. H. Kim (2017) Deep Learning from Scratch, p.312, Hanbit Media, pp.268-272, 2017. (In Korean)
8 C. Szegedy, W. Lui, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Ertan, V. Vanhoucke, A. Rabinovich, "Going Deeper with Convolutions," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1-9, 2015.
9 R. Roy. Using YOLOv3 for Real-Time Detection of PPE and Fire [Internet], Towards Data Science, c2020 [cited 2020 May 12], Available From: https://towardsdatascience.com/using-yolov3-for-real-time-detection-of-ppe-and-fire-1c671fcc0f0e (Accessed Oct. 29, 2020)
10 K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016.
11 W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu, A.C. Berg, "SSD: Single shot multibox detector," In Proceedings of European Conference on Computer Vision(ECCV), Amsterdam, Netherlands, pp.1-17, Oct. 2016. quoted in Park and Ko (2020) [18]
12 S. Wu, L. Zhang, "Using popular object detection methods for real time forest fire detection," In Proceedings of the 11th International Symposium on Computational Intelligence and Design (SCID), Hangzhou, China, pp.280-284, Dec. 2018. quoted in Park and Ko (2020) [18] DOI: https://doi.org/10.1109/ISCID.2018.00070   DOI
13 J. Redmon, A. Farhadi, "YOLOv3: Incremental Improvement," ArXiv Preprint arXiv:1804.02767, 2018. quoted in Park and Ko (2020) [18]
14 M. Park, B.C. Ko, "Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube," Sensors, vol.20, no.8, 2202, April 2020. DOI: https://doi.org/10.3390/s20082202   DOI
15 K. Muhammad, J. Ahmad, I. Mehmood, S. Rho, S. W. Baik, "Convolutional Neural Networks Based Fire Detection in Surveillance Videos," IEEE Transactions on Systems, Mand, and Cybernetics: Systems, vol.49, no. 7, pp.1419-1434, April 2018. DOI: https://doi.org/10.1109/TSMC.2018.2830099   DOI
16 K. Muhammad, J. Ahmad, I. Mehmood, S. W. Baik, "Early Fire Detection Using Convolutional Neural Networks during Surveillance for Effective Disaster Management", Neurocomputing, vol.288, pp.30-42, May 2018. quoted in Muhammad et al. (2018) [19] DOI: https://doi.org/10.1016/j.neucom.2017.04.083   DOI
17 T. Celik, H. Demirel, "Fire Detection in Video Sequences Using a Generic Color Model," Fire Safety Journal, vol.46, no.2, pp.147-158, 2009. quoted in Muhammad et al. (2018) [19] DOI: https://doi.org/10.1016/j.firesaf.2008.05.005   DOI
18 D. Y. T. Chino, L. P. S. Avalhais, J. F. Rodrigues, A. J. M. Traina, "BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis," in Proc. 28th SIBGRAPI Conf. Graph., Patterns Images, pp.95-102, August 2015. quoted in Muhammad et al. (2018) [19]
19 S. Rudz, K. Chetehouna, A. Hafiane, H. Laurent, O. Sero-Guillaume, "Investigation of a Novel Image Segmentation Method Dedicated to Forest Fire Applications," Meas. Sci. Technol., vol.24, no.7, p.075403, 2013. quoted in Muhammad et al. (2018) [19]   DOI
20 L. Rossi, M. Akhloufi, Y. Tison, "On the Use of Stereovision to Develop a Novel Instrumentation System to Extract Geometric Fire Fronts Characteristics," Fire Safety Journal, vol.46, pp.9-20, 2011. quoted in Muhammad et al. (2018) [19] DOI: https://doi.org/10.1016/j.firesaf.2010.03.001   DOI
21 S. Verstockt, T. Beji, P. D. Potter, S. V. Hoecke, B. Sette, B. Merci, R.V. Walle, "Video Driven Fire Spread Forecasting (f) Using Multi-Modal LWIR and Visual Flame and Smoke Data," Pattern Recognition Letters, vol.34, no.1, pp.62-69, 2013. quoted in Muhammad et al. (2018) [19] DOI: https://doi.org/10.1016/j.patrec.2012.07.018   DOI
22 B.C. Ko, S. J. Ham, J. Y. Nam, "Modeling and Formalization of Fuzzy Finite Automata for Detection of Irregular Fire Flames," IEEE Trans. Circuits Syst. Video Technol., vol.21, no.12, pp.1903-1912, Dec. 2011. quoted in Muhammad et al. (2018) [19] DOI: https://doi.org/10.1109/TCSVT.2011.2157190   DOI
23 K. J. Kim, Y. S. Park, S. W. Park, "Development of Artificial Neural Network Model for Estimation of Cable Tension of Cable-Stayed Bridge", Journal of the Korea Academia-Industrial Cooperation Society, vol.21, no.3, pp.414-419, March 2020. (In Korean) DOI: https://doi.org/10.5762/KAIS.2020.21.3.414   DOI
24 J. W. Shin, "Introduction of Recent Deep Learning Algorithms for Image Identification," The Journal of the Korea Institute of Communication Sciences, vol.34, no.7, pp.25-30, 2017. (In Korean)
25 S. Shim, S. I. Choi, "Development on Identification Algorithm of Risk Situation around Construction Vehicle Using YOLO-v3," Journal of the Korea Academia-Industrial Cooperation Society, vol.20, no.7, pp.622-629, July 2019. (In Korean) DOI: https://doi.org/10.5762/KAIS.2019.20.7.622   DOI
26 S. Chung, S. Moon, S. Chi, "Bridge Damage Factor Recognition from Inspection Reports Using Deep Learning," Journal of the Korean Society of Civil Engineers, vol.38, no.4, pp.612-625, August 2018. DOI: https://doi.org/10.12652/Ksce.2018.38.4.0621   DOI