A Study on the Development of an Automatic Classification System for Life Safety Prevention Service Reporting Images through the Development of AI Learning Model and AI Model Serving Server

doi:10.15683/kosdi.2023.6.30.432

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2023 Vol.19, Issue 2 Preview Page Next Page

Original Article

A Study on the Development of an Automatic Classification System for Life Safety Prevention Service Reporting Images through the Development of AI Learning Model and AI Model Serving Server AI 학습모델 및 AI모델 서빙 서버 개발을 통한 생활안전 예방 서비스 신고 이미지 자동분류 시스템 개발에 대한 연구: 정영식¹*ㆍ김용운²ㆍ임정일³
Young Sic Jeong¹*, Yong-Woon Kim², Jeongil Yim³; ¹Principal Researcher, Standard & Open Source Research Division, ETRI, Daejeon, Republic of Korea
²Principal Researcher, Standard & Open Source Research Division, ETRI, Daejeon, Republic of Korea
³Principal Engineer, Standard & Open Source Research Division, ETRI, Daejeon, Republic of Korea

30 June 2023. pp. 432-438

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Abstract

Purpose: The purpose of this study is to enable users to conveniently report risks by automatically classifying risk categories in real time using AI for images reported in the life safety prevention service app. Method: Through a system consisting of a life safety prevention service platform, life safety prevention service app, AI model serving server and sftp server interconnected through the Internet, the reported life safety images are automatically classified in real time, and the AI model used at this time An AI learning algorithm for generation was also developed. Result: Images can be automatically classified by AI processing in real time, making it easier for reporters to report matters related to life safety.Conclusion: The AI image automatic classification system presented in this paper automatically classifies reported images in real time with a classification accuracy of over 90%, enabling reporters to easily report images related to life safety. It is necessary to develop faster and more accurate AI models and improve system processing capacity.

Keywords

Classification

Model Serving

Model Serving Server

Sftp Server

Internet

Synchronization

연구목적: 생활안전 예방서비스 앱에서 신고되는 이미지를 AI를 사용하여 실시간으로 위험 카테고리를 자동으로 분류하여 사용자에게 편리한 위험신고를 가능하게 하는 것을 목적으로 한다. 연구방법: 인터넷으로 상호연결되는 생활안전 예방서비스 플랫폼, 생활안전 예방서비스 앱, AI 모델 서빙 서버와 sftp 서버로 구성되는 시스템을 통하여 신고된 생활안전 이미지를 실시간으로 자동분류하며, 이때 사용되는 AI모델 생성을 위한 AI 학습 알고리즘도 개발하였다. 연구결과: 이미지를 실시간으로 AI 처리하여 자동으로 분류할 수 있게 되어, 신고자가 생활안전 관련 사항을 보다 편리하게 신고할 수 있게 되었다. 결론: 본 논문에서 제시하는 AI 이미지 자동분류 시스템은 90% 이상의 분류 정확도로 신고 이미지를 실시간으로 자동분류하여 신고자가 간편하게 생활안전 관련 이미지를 신고할 수 있게 되었으며 향후 생활안전 예방서비스 앱의 사용자의 증가에 따라 더욱 빠르고 정확한 AI 모델 개발 및 시스템 처리용량 향상이 필요하다.

키워드

인공지능

분류

모델 서빙

모델 서빙 서버

sftp 서버

인터넷

동기화

References

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Lee, C.Y., Park, G.J., Kim, J., Kim, T. (2020). "A design of the social disasters safety platform based on the structured and unstructured data." Journal of the Society of Disaster Information, Vol. 16, No. 4, pp. 842-848.
Nam, G.-T., Seo, K.-J., Choi, D.-C. (2022). "A study on the development of AI-based fire fighting facility design technology." Journal of the Society of Disaster Information, Vol. 18, No. 4, pp. 883-890.
Roy, A., Anju, P., Tomy, L., Rajeswari, M. (2021). "Recent study on image denoising using deep CNN techniques." 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, pp. 1838-1843. 10.1109/ICACCS51430.2021.9441710
Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B. (2019). "HybridSN: Exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification." IEEE Geoscience and Remote Sensing Letters, Vol. 17, No. 2, pp. 277-281. 10.1109/LGRS.2019.2918719
Tahir, H., Shahbaz Khan, M., Owais Tariq, M. (2021). "Performance analysis and comparison of faster R-CNN, mask R-CNN and ResNet50 for the detection and counting of vehicles." 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, pp. 587-594. 10.1109/ICCCIS51004.2021.9397079
Yanagisawa, H., Yamashita, T., Watanabe, H. (2018). "A study on object detection method from manga images using CNN." 2018 International Workshop on Advanced Image Technology (IWAIT), Chiang Mai, Thailand, pp. 1-4. 10.1109/IWAIT.2018.8369633
Zainab, A.S.N., Soesanti, I., Utomo, D.R. (2022). "Detection of COVID-19 using CNN's deep learning method: Review." 2022 4th International Conference on Biomedical Engineering (IBIOMED), Yogyakarta, Indonesia, pp. 59-64. 10.1109/IBIOMED56408.2022.9988533

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 : 19
No :2
Pages :432-438
DOI :https://doi.org/10.15683/kosdi.2023.6.30.432

[1] Huang, J., Liu, Y., Xia, Y., Zhong, Z., Sun, J. (2019). "Train driving data learning with S-CNN model for gear prediction and optimal driving." 2019 Chinese Automation Congress (CAC), Hangzhou, China, pp. 2227-2232. 10.1109/CAC48633.2019.8996712

[2] Lee, C.Y., Park, G.J., Kim, J., Kim, T. (2020). "A design of the social disasters safety platform based on the structured and unstructured data." Journal of the Society of Disaster Information, Vol. 16, No. 4, pp. 842-848.

[3] Nam, G.-T., Seo, K.-J., Choi, D.-C. (2022). "A study on the development of AI-based fire fighting facility design technology." Journal of the Society of Disaster Information, Vol. 18, No. 4, pp. 883-890.

[4] Roy, A., Anju, P., Tomy, L., Rajeswari, M. (2021). "Recent study on image denoising using deep CNN techniques." 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, pp. 1838-1843. 10.1109/ICACCS51430.2021.9441710

[5] Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B. (2019). "HybridSN: Exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification." IEEE Geoscience and Remote Sensing Letters, Vol. 17, No. 2, pp. 277-281. 10.1109/LGRS.2019.2918719

[6] Tahir, H., Shahbaz Khan, M., Owais Tariq, M. (2021). "Performance analysis and comparison of faster R-CNN, mask R-CNN and ResNet50 for the detection and counting of vehicles." 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, pp. 587-594. 10.1109/ICCCIS51004.2021.9397079

[7] Yanagisawa, H., Yamashita, T., Watanabe, H. (2018). "A study on object detection method from manga images using CNN." 2018 International Workshop on Advanced Image Technology (IWAIT), Chiang Mai, Thailand, pp. 1-4. 10.1109/IWAIT.2018.8369633

[8] Zainab, A.S.N., Soesanti, I., Utomo, D.R. (2022). "Detection of COVID-19 using CNN's deep learning method: Review." 2022 4th International Conference on Biomedical Engineering (IBIOMED), Yogyakarta, Indonesia, pp. 59-64. 10.1109/IBIOMED56408.2022.9988533

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

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