The Strip Steel Surface Defect Recognition Based on Multiple Support Vector Hyper-Sphere with Feature and Sample Weights

2016 ◽  
Vol 87 (12) ◽  
pp. 1678-1685 ◽  
Author(s):  
Rongfen Gong ◽  
Chengdong Wu ◽  
Maoxiang Chu ◽  
Xiaoping Liu
Keyword(s):  
Steel Surface ◽  
Surface Defect ◽  
Support Vector ◽  
Strip Steel ◽  
Sample Weights ◽  
Multiple Support ◽  
Defect Recognition

scholarly journals Strip Steel Surface Defects Recognition Based on SOCP Optimized Multiple Kernel RVM

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Hou Jingzhong ◽  
Xia Kewen ◽  
Yang Fan ◽  
Zu Baokai
Keyword(s):  
Steel Surface ◽  
Surface Defect ◽  
Surface Defects ◽  
Recognition Rate ◽  
Strip Surface ◽  
Strip Steel ◽  
Practical Applications ◽  
Multiple Kernel ◽  
Low Efficiency ◽  
Defect Recognition

Strip steel surface defect recognition is a pattern recognition problem with wide applications. Previous works on strip surface defect recognition mainly focus on feature selection and dimension reduction. There are also approaches on real-time systems that mainly exploit the autocorrection within some given picture. However, the instances cannot be used in practical applications because of a bad recognition rate and low efficiency. In this paper, we study the intelligent algorithm of strip steel surface defect recognition, where the goal is to improve the accuracy and save running time. This problem is very important in various applications, especially the process testing of steel manufacturing. We propose an approach called the second-order cone programming (SOCP) optimized multiple kernel relevance vector machine (MKRVM), which can recognize strip surface defects much better than other methods. The method includes the model parameter estimation, training, and optimization of the model based on SOCP and the classification test. We compare our approach with existing methods on strip surface defect recognition. The results demonstrate that our proposed approach can improve the recognition accuracy and reduce the time costs of the strip surface defect.

Improved cross pattern approach for steel surface defect recognition

2020 ◽  
Vol 110 (11-12) ◽  
pp. 3091-3100
Author(s):  
Zoheir Mentouri ◽  
Hakim Doghmane ◽  
Abdelkrim Moussaoui ◽  
Hocine Bourouba
Keyword(s):  
Steel Surface ◽  
Surface Defect ◽  
Pattern Approach ◽  
Defect Recognition

Research of Solar Cell Surface Defect Detection System Based on Machine Vision

2013 ◽  
Vol 718-720 ◽  
pp. 532-536 ◽  
Author(s):  
Mei Lin Feng ◽  
Xian Juan Zhou ◽  
Jian Guo Yu
Keyword(s):  
Solar Cells ◽  
Solar Cell ◽  
Machine Vision ◽  
Surface Defect ◽  
Detection System ◽  
Support Vector ◽  
Inspection System ◽  
Concept Design ◽  
Surface Defect Detection ◽  
Defect Recognition

According to the surface quality problem of the solar cells, the machine vision detection system is designed. Concept design of the visual inspection system, hardware configuration and software work process are described in detail. In the experimental process, solar cell images are collected in the motion state, the image characteristics of all kinds of damage are extracted, and the least squares support vector machine algorithm is used to construct the solar cell defect recognition model, the intelligent detection and classification of the solar cells can be achieved. Practice dictates that the system is effective to detect the surface defect of solar cells, guide the production process and improve the quality of products.

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