# CNN을 이용한 딥러닝 기반 하수관 손상 탐지 분류 시스템

• Hassan, Syed Ibrahim (Department of Computer Science and Engineering, Sejong University) ;
• Dang, Lien-Minh (Department of Computer Science and Engineering, Sejong University) ;
• Im, Su-hyeon (Department of Computer Science and Engineering, Sejong University) ;
• Min, Kyung-bok (Department of Computer Science and Engineering, Sejong University) ;
• Nam, Jun-young (Department of Computer Science and Engineering, Sejong University) ;
• Moon, Hyeon-joon (Department of Computer Science and Engineering, Sejong University)
• Accepted : 2018.01.12
• Published : 2018.03.28

#### Abstract

We propose an automatic detection and classification system of sewer damage database based on artificial intelligence and deep learning. In order to optimize the performance, we implemented a robust system against various environmental variations such as illumination and shadow changes. In our proposed system, a crack detection and damage classification method using a deep learning based Convolutional Neural Network (CNN) is implemented. For optimal results, 9,941 CCTV images with $256{\times}256$ pixel resolution were used for machine learning on the damaged area based on the CNN model. As a result, the recognition rate of 98.76% was obtained. Total of 646 images of $720{\times}480$ pixel resolution were extracted from various sewage DB for performance evaluation. Proposed system presents the optimal recognition rate for the automatic detection and classification of damage in the sewer DB constructed in various environments.

#### Acknowledgement

Supported by : Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET)

#### References

1. M. D. Yang, T. C. Su, "Automated diagnosis of sewer pipe defects based on machine learning approaches,"Expert Systems with Applications, vol. 35, no. 3, pp. 1327-1337, October, 2008. https://doi.org/10.1016/j.eswa.2007.08.013
2. D. H. Koo, and S. T. Ariaratnam, "Innovative method for assessment of underground sewer pipe condition," Automation in construction, vol. 15, no. 4, pp. 479-488, July, 2006. https://doi.org/10.1016/j.autcon.2005.06.007
3. W. Zhang, Z. Zhang, D. Qi, and Y. Liu, "Automatic crack detection and classification method for subway tunnel safety monitoring," Sensors, vol. 14, no. 10, pp. 19307-19328, October, 2014. https://doi.org/10.3390/s141019307
4. T. C. Su, and M. D. Yang, "Application of morphological segmentation to leaking defect detection in sewer pipelines," Sensors, vol. 14, no. 5, pp. 8686-8704, May, 2014. https://doi.org/10.3390/s140508686
5. Y. J. Cha, W. Choi, and O. Buyukozturk, "Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks," Computer-Aided Civil and Infrastructure Engineering, vol. 32, no. 5, pp. 361-378, March, 2017. https://doi.org/10.1111/mice.12263
6. Z. Lei, et al, "Road crack detection using deep convolutional neural network," in International Conference on Image Processing on IEEE, Phoenix: AZ, pp. 3708-3712, Sept, 2016.
7. M. Osama, and S. E. Tariq, "Classification of defects in sewer pipes using neural networks," Journal of infrastructure systems, vol. 6, no. 3, pp. 97-104, Sept, 2000. https://doi.org/10.1061/(ASCE)1076-0342(2000)6:3(97)
8. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," In Advances in neural information processing systems, pp. 1097-1105, December, 2012.
9. N. Ejaz, I. Mehmood, S. W. Baik, "Efficient visual attention based framework for extracting key frames from videos," Signal Processing: Image Communication, vol. 28, no. 1, pp. 34-44, January, 2013. https://doi.org/10.1016/j.image.2012.10.002
10. O. Russakovsky, et al, "Imagenet large scale visual recognition challenge," International Journal of Computer Vision, vol. 115, no. 3, pp. 211-252, April, 2015. https://doi.org/10.1007/s11263-015-0816-y
11. M. K.Vairalkar, and S. U. Nimbhorkar, "Edge detection of images using sobel operator," International Journal of Emerging Technology and Advanced Engineering, vol. 2, no. 1, pp. 291-293, January, 2012.
12. P. Soille, Morphological image analysis: principles and applications, 2nd ed, New York, NY: Springer Science & Business Media, 2013.
13. I. Khalifa, A. E. Aboutabl, and G. S. A. Aziz, "A New Image Model for Predicting Cracks in Sewer Pipes based on Time," International Journal of Computer Applications, vol. 87, no. 9, pp. 25-32, February, 2014. https://doi.org/10.5120/15238-3779