Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine

2014 ◽  
Vol 38 ◽  
pp. 1-13 ◽  
Author(s):  
Mahmoud R. Halfawy ◽  
Jantira Hengmeechai
Keyword(s):  
Support Vector Machine ◽  
Defect Detection ◽  
Closed Circuit ◽  
Support Vector ◽  
Closed Circuit Television ◽  
Histograms Of Oriented Gradients

Automated defect detection tool for closed circuit television (cctv) inspected sewer pipelines

2018 ◽  
Vol 89 ◽  
pp. 99-109 ◽  
Author(s):  
Alaa Hawari ◽  
Mazen Alamin ◽  
Firas Alkadour ◽  
Mohamed Elmasry ◽  
Tarek Zayed
Keyword(s):  
Defect Detection ◽  
Closed Circuit ◽  
Closed Circuit Television ◽  
Detection Tool

scholarly journals An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine

2019 ◽  
Vol 52 (7-8) ◽  
pp. 1102-1110 ◽  
Author(s):  
Yu Wu ◽  
Yanjie Lu
Keyword(s):  
Support Vector Machine ◽  
Machine Vision ◽  
Defect Detection ◽  
Surface Defects ◽  
Vision System ◽  
Rapid Development ◽  
Training Model ◽  
Support Vector ◽  
Ideal Condition ◽  
Product Packaging

Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%.

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