Research on APC Verification for Disaster Victims and Vulnerable Facilities

doi:10.15683/kosdi.2024.3.31.199

All Issue

2024 Vol.20, Issue 1 Preview Page Next Page

Original Article

Research on APC Verification for Disaster Victims and Vulnerable Facilities 재난약자 및 취약시설에 대한 APC실증에 관한 연구: 김승용¹ㆍ황인철²*ㆍ김동식³ㆍ신정재⁴ㆍ용승갑⁵
Seungyong Kim¹, Incheol Hwang²*, Dongsik Kim³, Jungjae Shin⁴, Seunggap Yong⁵; ¹Professor, Department of Management Information System, Korea National University of Transportation, Chungju, Republic of Korea
²Director, Secuware Inc., Cheongju, Republic of Korea
³Vice Chairman, Terra International Management Company, Anyang, Republic of Korea
⁴Adjunct Professor, Department of Graduate School of Arts & Design, Kyonggi University, Suwon, Republic of Korea ⁵Department Manager, Secuware Inc., Cheongju, Republic of Korea

31 March 2024. pp. 199-205

PDF

Abstract

Purpose: This study aims to improve the recognition rate of Auto People Counting (APC) in accurately identifying and providing information on remaining evacuees in disaster-vulnerable facilities such as nursing homes to firefighting and other response agencies in the event of a disaster. Methods: In this study, a baseline model was established using CNN (Convolutional Neural Network) models to improve the algorithm for recognizing images of incoming and outgoing individuals through cameras installed in actual disaster-vulnerable facilities operating APC systems. Various algorithms were analyzed, and the top seven candidates were selected. The research was conducted by utilizing transfer learning models to select the optimal algorithm with the best performance. Results: Experiment results confirmed the precision and recall of Densenet201 and Resnet152v2 models, which exhibited the best performance in terms of time and accuracy. It was observed that both models demonstrated 100% accuracy for all labels, with Densenet201 model showing superior performance. Conclusion: The optimal algorithm applicable to APC among various artificial intelligence algorithms was selected. Further research on algorithm analysis and learning is required to accurately identify the incoming and outgoing individuals in disaster-vulnerable facilities in various disaster situations such as emergencies in the future.

Keywords

Disaster-vulnerable Facilities

Victims

Auto People Counter

Image Recognition

연구목적: 본 연구는 요양병원 등 재난취약시설에 재난이 발생할 경우 잔류한 요구조자를 정확하게 파악하여 소방 등 대응기관에 제공하는 APC(Auto People Counting)의 인식률 개선에 목적이 있다. 연구방법: 본 연구에서는 실제 재난취약시설에 설치되어 운영 중인 APC를 대상으로 카메라를 통해 출입 인원의 이미지를 인식하는 알고리즘을 개선하기 위해 CNN모델을 활용하여 베이스라인 모델링을 하였다. 다양한 알고리즘의 성능을 분석하여 상위 7개의 후보군을 선정하고 전이학습 모델을 활용하여 성능이 가장 우수한 최적의 알고리즘을 선정하는 방법으로 연구를 수행하였다. 연구결과: 실험결과 시간과 성능이 가장 좋은 Densenet201, Resnet152v2 모델의 정밀도와 재현율을 확인한 결과 모든 라벨에 대해서 정확도 100%를 나타내는 것을 확인할 수 있었다. 이 중 Densenet201 모델이 더 높은 성능을 보여주었다. 결론: 다양한 인공지능 알고리즘 중 APC에 적용할 수 있는 최적의 알고리즘을 선정하였다. 향후 연무 등 다양한 재난상황에서 재난취약시설 내 출입인원을 정확하게 파악할 수 있도록 알고리즘 분석 및 학습에 대한 추가 연구가 요구된다.

키워드

재난취약시설

요구조자

자동계수기

인공지능

영상인식

References

He, K., Zhang, X., Ren, S., Sun, J. (2015). "Deep residual learning for image recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770-778. 10.1109/CVPR.2016.90 26180094
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q. (2017). "Densely connected convolutional networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 4700-4708. 10.1109/CVPR.2017.243
Krizhevsky, A., Sutskever, I., Hinton, G.E. (2012). "ImageNet classification with deep convolutional neural networks." Communications of the ACM, Vol. 60 No. 6, pp. 84-90. 10.1145/3065386
Kwon, H., Youn, C.J., Kim, H.D. (2021). "Necessity of applying lifesaving smart technology for vulnerable class in nursing facilities: Focus on introduction of RFID-based positioning system and 3D scanning technology." Fire Science and Engineering, Vol. 35, No. 2, pp. 143-154. 10.7731/KIFSE.f8fccc2d
Lee, J.R., Lee, D.W., Jeong, S.H., Jeong, S. (2023). "Comparative study on artificial in intelligence model performance between image and video recognition in the fire detection area." Journal of the Society of Disaster Information, Vol. 19, No. 4, pp. 968-975.
Simonyan, K., Zisserman, A. (2014). "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A. (2014). "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 1-9. 10.1109/CVPR.2015.7298594
Tan, M., Le, Q. (2019). "EfficientNet: rethinking model scaling for convolutional neural networks." Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, USA, Vol. 97, pp. 6105-6114.

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 : 20
No :1
Pages :199-205
DOI :https://doi.org/10.15683/kosdi.2024.3.31.199

[1] He, K., Zhang, X., Ren, S., Sun, J. (2015). "Deep residual learning for image recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770-778. 10.1109/CVPR.2016.90 26180094

[2] Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q. (2017). "Densely connected convolutional networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 4700-4708. 10.1109/CVPR.2017.243

[3] Krizhevsky, A., Sutskever, I., Hinton, G.E. (2012). "ImageNet classification with deep convolutional neural networks." Communications of the ACM, Vol. 60 No. 6, pp. 84-90. 10.1145/3065386

[4] Kwon, H., Youn, C.J., Kim, H.D. (2021). "Necessity of applying lifesaving smart technology for vulnerable class in nursing facilities: Focus on introduction of RFID-based positioning system and 3D scanning technology." Fire Science and Engineering, Vol. 35, No. 2, pp. 143-154. 10.7731/KIFSE.f8fccc2d

[5] Lee, J.R., Lee, D.W., Jeong, S.H., Jeong, S. (2023). "Comparative study on artificial in intelligence model performance between image and video recognition in the fire detection area." Journal of the Society of Disaster Information, Vol. 19, No. 4, pp. 968-975.

[6] Simonyan, K., Zisserman, A. (2014). "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556.

[7] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A. (2014). "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 1-9. 10.1109/CVPR.2015.7298594

[8] Tan, M., Le, Q. (2019). "EfficientNet: rethinking model scaling for convolutional neural networks." Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, USA, Vol. 97, pp. 6105-6114.

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

All Issue