Deep Learning Based Underground Sewer Defect Classification Using a Modified RegNet
Source abstract
Abstract
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... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Water Systems and Optimization · Geotechnical Engineering and Underground Structures
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (31)
Show source-provided references
1. American Society of Civil Engineers (ASCE) Report card for America’s infrastructure Retrieved from: https://www.infrastructurereportcard.org/wpcontent/uploads/2017/01/astewater-Final.pdf, (accessed on 20 June 2022), 2017.
2. The relevance of sewer deterioration modelling to support asset management strategies10.1080/1573062X.2017.1325497 ↗
3. United States Environmental Protection Agency (EPA) Clean water sheds needs survey (CWNS) Retrieved from: https://www.epa.gov/cwns, (accessed on 20 June 2022), 2012.
4. Vision-based defect inspection and condition assessment for sewer pipes: S comprehensive survey
5. A robust instance segmentation framework for underground sewer defect detection10.1016/j.measurement.2022.110727 ↗
6. A survey on image-based automation of CCTV and SSET sewer inspections10.1016/j.autcon.2019.103061 ↗
7. DefectTR: End-to-end defect detection for sewage networks using a transformer10.1016/j.conbuildmat.2022.126584 ↗
8. Pixel-level tunnel crack segmentation using a weakly supervised annotation approach10.1016/j.compind.2021.103545 ↗
9. Integrated vision-based system for automated defect detection in sewer closed circuit television inspection videos10.1061/(ASCE)CP.1943-5487.0000312 ↗
10. Designing network design spaces
11. Deep residual learning for image recognition
12. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
13. Going deeper with convolutions
14. Visual pattern recognition supporting defect reporting and condition assessment of wastewater collection systems10.1061/(ASCE)0887-3801(2009)23:3(160) ↗
15. Automated detection of faults in sewers using CCTV image sequences10.1016/j.autcon.2018.08.005 ↗
16. Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city10.1007/s11783-019-1102-y ↗
17. Sewer pipeline fault identification using anomaly detection algorithms on video sequences10.1109/ACCESS.2020.2975887 ↗
18. Underground sewer pipe condition assessment based on convolutional neural networks10.1016/j.autcon.2019.102849 ↗
19. Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques10.1016/j.autcon.2018.08.006 ↗
20. Automatic detection and classification of sewer defects via hierarchical deep learning10.1109/TASE.2019.2900170 ↗
21. A defect classification methodology for sewer image sets with convolutional neural networks10.1016/j.autcon.2019.04.013 ↗
22. Robust sewer defect detection with text analysis based on deep learning10.1109/ACCESS.2022.3168660 ↗
23. Deep learning-based sewer defect classification for highly imbalanced dataset10.1016/j.cie.2021.107630 ↗
24. Convolutional neural networks–based model for automated sewer defects detection and classification10.1061/(ASCE)WR.1943-5452.0001394 ↗
25. A multi-defect detection system for sewer pipelines based on StyleGAN-SDM and fusion CNN10.1016/j.conbuildmat.2021.125385 ↗
26. C. Han, L. Zhu and S. Han, “Proxylessnas: Direct neural architecture search on target task and hardware,” arXiv preprint arXiv:1812.00332, 2018.
27. Modified convolutional neural network based on dropout and the stochastic gradient descent optimizer10.3390/a11030028 ↗
28. Smartphone-based bulky waste classification using convolutional neural networks10.1007/s11042-020-09571-5 ↗
29. Sewer damage detection from imbalanced CCTV inspection data using deep convolutional neural networks with hierarchical classification10.1016/j.autcon.2019.01.017 ↗
30. Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks10.1016/j.autcon.2018.03.028 ↗
31. Automated sewer defects detection using style-based generative adversarial networks and fine-tuned well-known CNN classifier10.1109/ACCESS.2021.3073915 ↗