论文信息 - Supporting the Process of Sewer Pipes Inspection Using Machine Learning on Embedded Devices - 字舞流文

Supporting the Process of Sewer Pipes Inspection Using Machine Learning on Embedded Devices

Tomasz Szydlo | Mieszko Klusek

[1] Mingzhu Wang,et al. Development and Improvement of Deep Learning Based Automated Defect Detection for Sewer Pipe Inspection Using Faster R-CNN , 2018, EG-ICE.

[2] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[3] Joakim Bruslund Haurum,et al. A Survey on Image-Based Automation of CCTV and SSET Sewer Inspections , 2020 .

[4] Mahmoud R. Halfawy,et al. Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine , 2014 .

[5] Dawei Li,et al. Automatic Detection and Classification of Sewer Defects via Hierarchical Deep Learning , 2019, IEEE Transactions on Automation Science and Engineering.

[6] Alireza Bayat,et al. Productivity Improvement of Sewer CCTV Inspection through Time Study and Route Optimization , 2015 .

[7] F. Cherqui,et al. The consistency of visual sewer inspection data , 2013 .

[8] François Clemens,et al. A defect classification methodology for sewer image sets with convolutional neural networks , 2019, Automation in Construction.

[9] J. Mashford,et al. A morphological approach to pipe image interpretation based on segmentation by support vector machine , 2010 .