지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.12
- 수록면
- 595 - 602 (8page)
- DOI
- 10.7848/ksgpc.2022.40.6.595
이용수
초록· 키워드
오류제보하기
Ports are one of the important social infrastructures that function as central transportation hubs for goods. Although fast and continuous monitoring is essential for efficient infrastructure management, it has been time and labor consuming with human’s direct inspection. Effective monitoring, including real-time monitoring, has drawn significant attention since the advent of UAVs (Unmanned Aerial Vehicles), robotics, IoT (Internet of Things), and AI (Artificial Intelligence) technologies. In this study, we develop a deep learning-based change detection model for multitemporal images acquired from UAV’s for breakwater facility. SNUNet-CD algorithm, a combination of Siamese Network and NestedUnet, was used for detecting changes in dissipating blocks in the facility, and four data collection schemes were tested for the study site of Yongho Port, located in Busan, South Korea. The results showed that accuracy performance is around 0.8 in precision, recall, and F1-score, when the model was trained with site-specific image data sets fortified by data augmentation.
목차
Abstract
1. Introduction
2. Methodology
3. Results and Analysis
4. Conclusion
References
참고문헌 (9)
참고문헌 신청
Cao, J., Liao, J., Zhang, B., Wang, K., and Zhao, W. (2021). Object-based change detection of very high-resolution remote sensing images incorporating multiscale uncertainty analysis by fusing pixel-based change detection. Journal of Electronic Imaging, 30(5), 053003. https://doi.org/10.1117/1.JEI.30.5.053003
Dementyiev, V. E., Andriyanov, N. A., and Vasilyiev, K. K. (2020). Use of images augmentation and implementation of doubly stochastic models for improving accuracy of recognition algorithms based on convolutional neural networks. 2020 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO), 1-4. https://doi.org/10.1109/SYNCHROINFO49631.2020.9166000
Fang, S., Li, K., Shao, J., and Li, Z. (2022). SNUNet-CD: A densely connected siamese network for change detection of VHR images. IEEE Geoscience and Remote Sensing Letters, 19. https://doi.org/10.1109/LGRS.2021.3056416
Izaguirre, C., Losada, I. J., Camus, P., Vigh, J. L., and Stenek, V. (2021). Climate change risk to global port operations. Nature Climate Change, 11(1), 14-20.
Lebedev, M. A., Vizilter, Y. v, Vygolov, O. v, Knyaz, V. A., and Rubis, A. Y. (2018). Change detection in remote sensing images using conditional adversarial networks. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42(2). https://doi.org/10.5194/isprs-archives-XLII-2-565-2018
Dementyiev, V. E., Andriyanov, N. A., and Vasilyiev, K. K. (2020). Use of images augmentation and implementation of doubly stochastic models for improving accuracy of recognition algorithms based on convolutional neural networks. 2020 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO), 1-4. https://doi.org/10.1109/SYNCHROINFO49631.2020.9166000
Fang, S., Li, K., Shao, J., and Li, Z. (2022). SNUNet-CD: A densely connected siamese network for change detection of VHR images. IEEE Geoscience and Remote Sensing Letters, 19. https://doi.org/10.1109/LGRS.2021.3056416
Izaguirre, C., Losada, I. J., Camus, P., Vigh, J. L., and Stenek, V. (2021). Climate change risk to global port operations. Nature Climate Change, 11(1), 14-20.
Lebedev, M. A., Vizilter, Y. v, Vygolov, O. v, Knyaz, V. A., and Rubis, A. Y. (2018). Change detection in remote sensing images using conditional adversarial networks. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42(2). https://doi.org/10.5194/isprs-archives-XLII-2-565-2018
함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
CNN-based Unwanted UAV Detection System
한국통신학회 학술대회논문집
2021 .06
-
건설분야에서의 UAV (Unmanned Aerial Vehicle) 활용 연구 동향
건설관리 : 한국건설관리학회 학회지
2016 .01
-
A Study of Path-Finding Method of Small Unmanned Aerial Vehicles for Collision Avoidance
한국위성정보통신학회논문지
2017 .03
-
Study on automatic change detection using UAV and aerial images
한국측량학회 학술대회자료집
2022 .04
-
Comparative Analysis between Deep Learning Algorithms for Matching Aerial Photography and Unmanned Aerial Vehicle Images
대한토목학회 학술대회
2023 .10
-
무인기 환경에서의 Deep-Learning 기반 번호판 검출 알고리즘 구현
항공우주시스템공학회 학술행사 논문집
2018 .04
-
A Human Detection Model for UAV-Assisted Emergency Management Information Systems
한국정보과학회 학술발표논문집
2020 .07
-
UAV Real-time Object Detection and Tracking for Moving object based Deep learning
한국항공우주학회 학술발표회 초록집
2023 .04
-
심층 학습 네트워크 이용한 항공 영상 객체의 방향 정보 regression
대한전자공학회 학술대회
2021 .06
-
불확실성을 이용한 딥러닝 기반의 항공 이미지 객체 탐지
제어로봇시스템학회 논문지
2020 .11
-
3D Path Planning of Unmanned Aerial Vehicles
한국통신학회 학술대회논문집
2020 .02
-
Study on the Integrated Water System Management based on Unmanned Aerial Vehicle using Image Processing Technology
대한환경공학회 학술발표논문집
2017 .11
-
Current approaches of artificial intelligence in breakwaters - A review
Ocean systems engineering
2017 .01
-
무인 항공기(UAV)와 AI 머신러닝기술을 활용한 구조물의 안전진단 기술 개발
대한토목학회지
2020 .01
-
Convolutional Neural Networks for Analyzing Unmanned Aerial Vehicles Sound
제어로봇시스템학회 국제학술대회 논문집
2018 .10
-
농업분야 무인항공기(UAV) 활용 연구동향 분석
산업경영시스템학회지
2020 .01
-
무인항공기 항공이미지 객체탐지 알고리즘 비교분석에 관한 연구
항공우주시스템공학회 학술행사 논문집
2023 .10
-
Enhancing UAV flight with IoT technology for remote area transportation
대한산업공학회 춘계공동학술대회 논문집
2019 .04
-
공중 통신 환경에서 Message Passing Algorithm 기반 다중 UAV 신호 검출 기법
전자공학회논문지
2024 .11
-
Deep Learning-Based Aerial Image Processing for Pavement Damage Monitoring
대한토목학회 학술대회
2023 .10
이 논문의 저자 정보
이 논문과 함께 이용한 논문
최근 본 자료
댓글(0)
0