- Volume 22 Issue 3
DOI QR Code
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
Supported by : Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET)
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