A Study on AI-Based Fire Protection System Design Automation Technology using CNN

doi:10.15683/kosdi.2025.3.31.186

All Issue

2025 Vol.21, Issue 1 Preview Page Next Page

Original Article

A Study on AI-Based Fire Protection System Design Automation Technology using CNN CNN을 활용한 AI 기반 소방 설계 자동화 기술 연구: 김민규¹ㆍ고세영²ㆍ최두찬³*
Min Gyu Kim¹, Se Young Ko², Doo Chan Choi³*; ¹Researcher, Future Initiative R&D Center, KF UBIS Co., Ltd, Seoul, Republic of Korea
²Doctor Candidate, Department of Construction Safety Engineering, Kyonggi University, Suwon, Republic of Korea
³Chief Executive Officer, KF UBIS Co., Ltd, Seoul, Republic of Korea

31 March 2025. pp. 186-192

PDF

Abstract

Purpose: The purpose is to reduce the time required through automation of fire design technology and increase design accuracy through AI. Method: Research and development for automation of fire design is conducted through the process of learning design drawings through AI and processing them using CNN algorithms. Result: It showed a time reduction effect of at least 17% and at most 95%, and it was proven that it produced results that are suitable for the current laws. Conclusion: Additional research and development will be conducted on a law learning algorithm using NLP for the possibility of changes in laws and application in overseas situations, and future research will be conducted to add more functions for more complete results.

Keywords

Fire System Design

Image Recognition

Design Automation

Artificial Intelligence

Convolutional Neural Network

연구목적: 소방 설계 기술의 자동화를 통해 소요 시간을 감축하고 AI를 이용해 설계 정확도를 높이는 것을 목적으로 한다. 연구방법: 설계 도면을 인공지능을 통해 학습하고, 이를 CNN 알고리즘을 활용하여 처리하는 과정을 통해 소방 설계의 자동화를 위한 연구 개발을 진행한다. 연구결과: 적게는 17%, 많게는 95%의 시간 감축 효과를 보였으며, 현행 법령에 걸맞은 결과물을 도출함을 증명하였다. 결론: 법령의 변경 가능성과 해외 상황에서의 적용을 위해 NLP를 활용한 법령 학습 알고리즘을 추가 연구개발 진행하고, 더 많은 기능을 추가하는 연구를 진행하여 더욱 완성도 높은 결과물을 위한 후속 연구를 진행 할 것이다.

키워드

소방 시설 설계

이미지 인식

설계 자동화

인공지능

합성곱 신경망

References

Hong, S.-H., Choi, D.-C., Lee, K.-H. (2020). "Analysis of literature review and automated design for fire protection facilities." Proceedings of the SAREK 2020 Winter Annual Conference, pp. 320-323.
Isaac, P.B., Diego, M.S., Manuel, E.A., Estefania, C.A. (2020). "A fast and accurate deep learning method for strawberry instance segmentation." Computers and Electronics in Agriculture, Vol. 178, 105736. 10.1016/j.compag.2020.105736
LeCun, Y. Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D. (1989). "Backpropagation applied to handwritten zip code recognition." Neural Computation, Vol. 1, No. 4, pp. 541-551. 10.1162/neco.1989.1.4.541
Lui, D., Jia, Z., Jin, M., Liu, Q., Liao, Z., Zhong, J., Ye, H., Chen, G. (2020). "Cardiac magnetic resonance image segmentation based on convolutional neural network." Computer Methods and Programs in Biomedicine, Vol. 197, 105755. 10.1016/j.cmpb.2020.105755 32977180
Sharma, P., Berwal, Y.P.S., Ghai, W. (2019). "Performance analysis of deep learning CNN models for disease detection in plants using image segmentation." Information Processing in Agriculture, Vol. 7, No. 4, pp. 566-574. 10.1016/j.inpa.2019.11.001
Yeon, S.-H., Kim, C.-H., Park, K.-W., Choi, D.-C., Jo, Y.-G., Choi, J-.M., Lee, K.-H. (2023). "Automated extraction of fire protection system design information from architectural drawings using artificial intelligence." Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 35, No. 7, pp. 331-342. 10.6110/KJACR.2023.35.7.331

Information

Publisher :The Korean Society of Disaster Information
Publisher(Ko) :한국재난정보학회
Journal Title :Journal of the Society of Disaster Information
Journal Title(Ko) :한국재난정보학회논문집
Volume : 21
No :1
Pages :186-192
DOI :https://doi.org/10.15683/kosdi.2025.3.31.186

[1] Hong, S.-H., Choi, D.-C., Lee, K.-H. (2020). "Analysis of literature review and automated design for fire protection facilities." Proceedings of the SAREK 2020 Winter Annual Conference, pp. 320-323.

[2] Isaac, P.B., Diego, M.S., Manuel, E.A., Estefania, C.A. (2020). "A fast and accurate deep learning method for strawberry instance segmentation." Computers and Electronics in Agriculture, Vol. 178, 105736. 10.1016/j.compag.2020.105736

[3] LeCun, Y. Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D. (1989). "Backpropagation applied to handwritten zip code recognition." Neural Computation, Vol. 1, No. 4, pp. 541-551. 10.1162/neco.1989.1.4.541

[4] Lui, D., Jia, Z., Jin, M., Liu, Q., Liao, Z., Zhong, J., Ye, H., Chen, G. (2020). "Cardiac magnetic resonance image segmentation based on convolutional neural network." Computer Methods and Programs in Biomedicine, Vol. 197, 105755. 10.1016/j.cmpb.2020.105755 32977180

[5] Sharma, P., Berwal, Y.P.S., Ghai, W. (2019). "Performance analysis of deep learning CNN models for disease detection in plants using image segmentation." Information Processing in Agriculture, Vol. 7, No. 4, pp. 566-574. 10.1016/j.inpa.2019.11.001

[6] Yeon, S.-H., Kim, C.-H., Park, K.-W., Choi, D.-C., Jo, Y.-G., Choi, J-.M., Lee, K.-H. (2023). "Automated extraction of fire protection system design information from architectural drawings using artificial intelligence." Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 35, No. 7, pp. 331-342. 10.6110/KJACR.2023.35.7.331

Journal of the Society of Disaster Information ISSN:1976-2208(Print) 2671-5287(Online) 한국재난정보학회논문집

All Issue