참고문헌
- National fire agency 119, Fire statistical yoarbook 2021, https://nfds.go.kr/, 2022
- Jun Seon Choi and Young Hoon Joo, "Fire Detection Method Using CCTV-based Flame Features and Density-based Spatial Clustering", The Transactions of the Korean Institute of Electrical Engineers, vol. 71, no. 4, pp. 656~662, 2022 https://doi.org/10.5370/KIEE.2022.71.4.656
- Turgay Celik and Hasan Demirel, "Fire detection in video sequences using a generic color model", Fire Safety Journal 44, pp. 147-158, 2009 https://doi.org/10.1016/j.firesaf.2008.05.005
- Yongtae Do, "Visual Sensing of Fires Using Color and Dynamic Features", Journal of Sensor Science and Technology Vol. 21, No. 3 pp. 211-216, 2012 https://doi.org/10.5369/JSST.2012.21.3.211
- Young-Jin Kim.Eun-Gyung Kim, "Image based Fire Detection using Convolutional Neural Network", 한국정보통신학회논문지(J. Korea Inst. Inf. Commun. Eng.) Vol. 20, No. 9 : 1649~1656 Sep. 2016
- Sangmin Suh, "Convolutional Neural Network Based Fire Detection Systems in Surveillance Camera", Journal of Knowledge Information Technology and Systems(JKITS), Vol. 16, No. 3, pp. 423-432, June 2021
- Nguyen Manh Dung, Soonghwan Ro, "Smoke Detection Algorithm Using Deep Learning", The Journal of Korean Institute of Communications and Information Sciences '17-07 Vol.42 No.07, 2017
- Ryu, Jinkyu, and Kwak, Dongkurl, "A Study on Flame and Smoke Detection Algorithm Using Convolutional Neural Network Based on Deep Learning", J. Korean Soc. Hazard Mitig. Vol. 20, No. 1,, pp.223~232, Feb. 2020 https://doi.org/10.9798/KOSHAM.2020.20.1.223
- Jihyeon Yim, Hyunho Park, Wonjae Lee, Seonghyun Kim, Yong-Tae Lee University of Science and Technology Electronics and Telecommunication Research Institute, "Deep Learning Based CCTV Fire Detection System", 한국방송.미디어공학회 추계학술대회, 2017
- *Ji-Hoon Shin, Yong-Min Park, Tae-Hwan Kim, "Fire Detection Based on Convolutional Neural Network", 대한전자공학회 학술대회709 - 712 (4 pages), Jun. 2017
- Young-Jin Kim.Eun-Gyung Kim, "Real-Time Fire Detection based on CNN and Grad-CAM", Journal of the Korea Institute of Information and Communication Engineering, Vol. 22, No. 12: 1596~1603, Dec. 2018 https://doi.org/10.6109/JKIICE.2018.22.12.1596
- Jianchen Miao; Guangyuan Zhao; Yue Gao; Yinzhe Wen, "Fire Detection Algorithm Based on Improved YOLOv5", International Conference on Control, Automation and Information Sciences (ICCAIS), Oct. 2021
- Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., & Xu, C. "Ghostnet: More features from cheap operations." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1580-1589)
- Wang, Z., Wu, L., Li, T., & Shi, P., "A Smoke Detection Model Based on Improved YOLOv5. Mathematics", 10(7), 1190, 2022 https://doi.org/10.3390/math10071190
- Xudong Dong, Shuai Yan, Chaoqun Duan, "A lightweight vehicles detection network model based on YOLOv5" Engineering Applications of Artificial Intelligence, 2022
- Qingqing Xu, Zhiyu Zhu , Huilin Ge , Zheqing Zhang, Xu Zang, "Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction", Computational and Mathematical Methods in Medicine, 9 pages, 2021
- SKYREX Co.,Ltd. Web site : http://www.skyrex.co.kr/main/main.php