Damage Detection and Classification System for Sewer Inspection using Convolutional Neural Networks based on Deep Learning

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)
  • Received : 2017.11.29
  • Accepted : 2018.01.12
  • Published : 2018.03.28


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.


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


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