Deep learning-based sewer defect classification for highly imbalanced dataset
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Deep learning-based sewer defect classification for highly imbalanced dataset
Authors
Keywords
Sewer network, Crack classification, Deep learning, CCTV, Text recognition, Imbalanced data
Journal
COMPUTERS & INDUSTRIAL ENGINEERING
Volume 161, Issue -, Pages 107630
Publisher
Elsevier BV
Online
2021-08-20
DOI
10.1016/j.cie.2021.107630
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A Survey on Image-Based Automation of CCTV and SSET Sewer Inspections
- (2020) Joakim Bruslund Haurum et al. AUTOMATION IN CONSTRUCTION
- Tampered and Computer-Generated Face Images Identification Based on Deep Learning
- (2020) L. Minh Dang et al. Applied Sciences-Basel
- A novel data-driven nonlinear solver for solid mechanics using time series forecasting
- (2020) Tan N. Nguyen et al. FINITE ELEMENTS IN ANALYSIS AND DESIGN
- Smartphone-based bulky waste classification using convolutional neural networks
- (2020) Hanxiang Wang et al. MULTIMEDIA TOOLS AND APPLICATIONS
- Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
- (2019) Abhronil Sengupta et al. Frontiers in Neuroscience
- Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city
- (2019) Xiangyang Ye et al. Frontiers of Environmental Science & Engineering
- A defect classification methodology for sewer image sets with convolutional neural networks
- (2019) Dirk Meijer et al. AUTOMATION IN CONSTRUCTION
- Face image manipulation detection based on a convolutional neural network
- (2019) L. Minh Dang et al. EXPERT SYSTEMS WITH APPLICATIONS
- Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
- (2019) Husein Perez et al. SENSORS
- Underground sewer pipe condition assessment based on convolutional neural networks
- (2019) Syed Ibrahim Hassan et al. AUTOMATION IN CONSTRUCTION
- Automatic Detection and Classification of Sewer Defects via Hierarchical Deep Learning
- (2019) Qian Xie et al. IEEE Transactions on Automation Science and Engineering
- Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks
- (2018) Srinath S. Kumar et al. AUTOMATION IN CONSTRUCTION
- Utilizing text recognition for the defects extraction in sewers CCTV inspection videos
- (2018) L. Minh Dang et al. COMPUTERS IN INDUSTRY
- Automated detection of faults in sewers using CCTV image sequences
- (2018) Joshua Myrans et al. AUTOMATION IN CONSTRUCTION
- Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques
- (2018) Jack C.P. Cheng et al. AUTOMATION IN CONSTRUCTION
- Learning from class-imbalanced data: Review of methods and applications
- (2017) Guo Haixiang et al. EXPERT SYSTEMS WITH APPLICATIONS
- Online Bagging and Boosting for Imbalanced Data Streams
- (2016) Boyu Wang et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Visual Pattern Recognition Supporting Defect Reporting and Condition Assessment of Wastewater Collection Systems
- (2009) W. Guo et al. JOURNAL OF COMPUTING IN CIVIL ENGINEERING
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now