Deep Learning Based Underground Sewer Defect Classification Using a Modified RegNet  

作　　者：Yu Chen Sagar A.S.M.Sharifuzzaman Hangxiang Wang Yanfen Li L.Minh Dang Hyoung-Kyu Song Hyeonjoon Moon 

机构地区：[1]Department of Computer Science and Engineering,Sejong University,Seoul,05006,Korea [2]Department of Intelligent Mechatronics Engineering,Sejong University,Seoul,05006,Korea [3]Department of Information of Communication Engineering,and Convergence Engineering for Intelligent Drone,Sejong University,Seoul,05006,Korea

出　　处：《Computers, Materials & Continua》2023年第6期5451-5469,共19页计算机、材料和连续体（英文）

基　　金：supported by Basic ScienceResearch Program through the National Research Foundation ofKorea(NRF)funded by the Ministry of Education(2020R1A6A1A03038540);by Korea Institute of Planning and Evaluation for Technology in Food,Agriculture,Forestry and Fisheries(IPET)through Digital Breeding Transformation Technology Development Program,funded by Ministry of Agriculture,Food and Rural Affairs(MAFRA)(322063-03-1-SB010);by the Technology development Program(RS-2022-00156456)funded by the Ministry of SMEs and Startups(MSS,Korea).

摘　　要：The sewer system plays an important role in protecting rainfall and treating urban wastewater.Due to the harsh internal environment and complex structure of the sewer,it is difficult to monitor the sewer system.Researchers are developing different methods,such as the Internet of Things and Artificial Intelligence,to monitor and detect the faults in the sewer system.Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects.However,the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for classification is very small,which can affect the robustness of the model in the constraint environment.As a result,this paper proposes a sewer condition monitoring framework based on deep learning,which can effectively detect and evaluate defects in sewer pipelines with high accuracy.We also introduce a large dataset of sewer defects with 20 different defect classes found in the sewer pipeline.This study modified the original RegNet model by modifying the squeeze excitation(SE)block and adding the dropout layer and Leaky Rectified Linear Units(LeakyReLU)activation function in the Block structure of RegNet model.This study explored different deep learning methods such as RegNet,ResNet50,very deep convolutional networks(VGG),and GoogleNet to train on the sewer defect dataset.The experimental results indicate that the proposed system framework based on the modified-RegNet(RegNet+)model achieves the highest accuracy of 99.5 compared with the commonly used deep learning models.The proposed model provides a robust deep learning model that can effectively classify 20 different sewer defects and be utilized in real-world sewer condition monitoring applications.

关 键 词：Deep learning defect classification underground sewer computer vision convolutional neural network RegNet 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]