CNN-LSTM 합성모델에 의한 하수관거 균열 예측모델
Short-Term Crack in Sewer Forecasting Method Based on CNN-LSTM Hybrid Neural Network Model
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초록
본 연구에서는 하수관거 내부에서 촬영된 균열 데이터를 활용하여 균열검출에 대한 시계열 예측 성능을 개선하기 위해GoogleNet의 전이학습과 CNN- LSTM(Long Short-Term Memory) 결합 방법을 제안하였다. LSTM은 합성곱방법(CNN)의장기의존성 문제를 해결할 수 있으며 공간 및 시간적 특징을 동시에 모델링 할 수 있다. 제안 방법의 성능을 검증하기 위해하수관거 내부 균열 데이터를 활용하여 학습데이터, 초기학습률 및 최대 Epochs를 변화하면서 RMSE를 비교한 결과 모든시험 구간에서 제안 방법의 예측 성능이 우수함을 알 수 있다. 또한 데이터가 발생하는 시점에 대한 예측 성능을 살펴본결과 역시 제안방법이 우수하게 나타나 균열검출의 예측에서 제안 방법이 효율적인 것을 검증하였다. 기존 합성곱방법(CNN) 단독 모델과 비교함으로써 본 연구를 통해 확보된 제안 방법과 실험 결과를 활용할 경우 콘크리트 구조물의 균열데이터뿐만아니라 시계열 데이터가 많이 발생하는 환경, 인문과학 등 다양한 영역에서 응용이 가능하다.
Abstract
In this paper, we propose a GoogleNet transfer learning and CNN-LSTM combination method to improve the time-series prediction performance for crack detection using crack data captured inside the sewer pipes. LSTM can solve the long-term dependency problem of CNN, so spatial and temporal characteristics can be considered at the same time. The predictive performance of the proposed method is excellent in all test variables as a result of comparing the RMSE(Root Mean Square Error) for time series sections using the crack data inside the sewer pipe. In addition, as a result of examining the prediction performance at the time of data generation, the proposed method was verified that it is effective in predicting crack detection by comparing with the existing CNN-only model. If the proposed method and experimental results obtained through this study are utilized, it can be applied in various fields such as the environment and humanities where time series data occurs frequently as well as crack data of concrete structures.
참고문헌 (25)
- Aizenberg, I., "Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications", pp.81~137 (2000), Springer Science & Business Media
- Bengio, Y., "Artificial Neural Networks and their Application to Speech", Sequence Recognition, McGill University, 1991
- Bengio, Y., "Neural Networks, Tricks of the Trade", pp.437~478 (2012)
- Dang, L. M., "Deep learning-based sewer defect classification for highly imbalanced dataset", Computers &Industrial Engineering, 161, pp.107630~ (2021)
- De Carvalho, A. C. L. F., "An integrated Boolean neural network for pattern classification", Pattern Recognition Letters, 15(8), pp.807~813 (1994)
- Learning while searching inconstraintsatisfaction problems, "Dechter, R.", University of California, Computer Science Department, Cognitive Systems Laboratory, 178~183 (1986)
- Deng, L., "Analysis of correlation structure for a neural predictive model with applications to speech recognition", Neural Networks, 7(2), pp.331~339 (1994)
- Fukushima, K., "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position", Biol, Cybern, 36(4), pp.193~202 (1980)
- Hinton, G. E., "Learning multiple layers of representation", Trends in Cognitive Sciences, 11(10), pp.428~434 (2007)
- Hinton, G. E., "The wake-sleep algorithm for unsupervised neural networks", 268(5214), pp.1158~1161 (1995)
- Hinton, G. E., "A Fast Learning Algorithm for Deep Belief Nets", Neural Computation, 18(7), pp.1527~1554 (2006)
- Hochreiter, S., "Untersuchungen zu dynamischen neuronalen Netzen Archived 2015-03-06atthe Wayback Machine", pp.1991
- 황철현, "Detection and Correction Method of Erroneous Data Using Quantile Pattern and LSTM", Journal of Information and Communication Convergence Engineering, 16(4), pp.242~247 (2018), 한국정보통신학회[DOI]
- Ivakhnenko, A. G., "Polynomial theory of complex systems", IEEE Transactions on Systems, Man and Cybernetics, 4, pp.364~378 (1971)
- Ivakhnenko, A. G., "Cybernetics and forecasting techniques" (1967), American Elsevier
- Jiang, J., "A deep learning algorithm for multi-source data fusion to predict water quality of urban sewer networks", Journal of Cleaner Production, 318(10), pp.2411~2502 (2021)
- McKim, R. A., "Condition assessment of underground sewer pipes using a modified digital image processing paradigm", Tunnelling and Underground Space Technology, 14, pp.29~37 (1999)
- Moselhi, O., "Automated detection of surface defects in water and sewer pipes", Automation in Construction, 8(5), pp.581~588 (1999)
- Van Quan Nguyen, "LSTM-based Anomaly Detection on Big Data for Smart Factory Monitoring", 디지털콘텐츠학회논문지, 19(4), pp.789~799 (2018), 한국디지털콘텐츠학회[DOI]
- Samuel, A. L., "Some Studies in Machine Learning Using the Game of Checkers Offsite Link", IBM Journal of Research and Development, 3(3), pp.206~226 (1959)
- 손병직, "CNN 기법을 이용한 저해상도 하수관거의 균열 인식", 한국복합신소재구조학회 논문집, 8(4), pp.58~65 (2017), 한국복합신소재구조학회[DOI]
- Wang, M. Z., "Automated sewer pipe defect tracking in CCTV videos based on defect detection and metric learning", Automation in Construction, 121, pp.103438~ (2021)
- Xu, K., "Sewer pipe deformation assessment by image analysis of video surveys", Pattern Recognition, 31(2), pp.169~180 (1998)
- Yang, M. D., "Automated diagnosis of sewer pipe defects based on machine learning approaches", Expert Systems with Applications, 35(3), pp.1327~1337 (2008)
- Yang, M. D., "Systematic image quality assessment for sewer inspection", Expert Systems with Applications, 38(3), pp.1766~1776 (2011)