Surface Defect Classification of Steel Strip with Few Samples Based on Dual‐Stream Neural Network

2021 ◽  
Author(s):  
Jiaqiao Zhang ◽  
Shihao Li ◽  
Yan Yan ◽  
Zhonghua Ni
Keyword(s):  
Neural Network ◽  
Surface Defect ◽  
Steel Strip ◽  
Defect Classification

Recognition and Classification of Hot Strip Surface Defect Based on Binary Tree SVM

2012 ◽  
Vol 538-541 ◽  
pp. 427-430 ◽  
Author(s):  
An Na Wang ◽  
Chao Hu ◽  
Chang Liang Xue ◽  
Hong Rui Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Binary Tree ◽  
Surface Defect ◽  
Surface Defects ◽  
Defect Classification ◽  
Strip Surface ◽  
Hot Strip ◽  
Classification Time

The paper presents a new method which uses Binary Tree SVM in the automatic classification of surface defects for hot strip. Two types of Binary Tree SVMs are applied in defect classification. Compared with BP neural network and one-against-one SVM, the algorithm adopted in the paper greatly improved the accuracy of classification and decreased the classification time.

The Defect Classification of TFT-LCD Array Photolithography Process via Using Back-Propagation Neural Network

2013 ◽  
Vol 378 ◽  
pp. 340-345
Author(s):  
Shih Feng Chen ◽  
Chin Chih Lai
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Defect Classification ◽  
Large Area ◽  
Fifth Generation ◽  
Network Training ◽  
Optical Inspection ◽  
Photolithography Process

This research is conducted mainly by using the Auto Optical Inspection (AOI) in the fifth generation TFT-LCD factory. In the development of detect-classification system, we designed the back-propagation neural network which combined with Visual Basic as the interface and MATLAB as an image-processing tool. The system is able to determine and display the detected results. The defect classification mainly designed to detect and classify the following defects: the second layer of the photo resist residue (AS-Residue), the second layer of large-area photo resist residue (AS-BPADJ), and the third layer of photo resist residue (M2-residue) in the Array Photolithography Process. Finally, the result is shown the fact that without the complicated processing procedures, the four defects in the TFT-LCD Array Photo Process can be precisely and quickly classified by imaging processing and back-propagation neural network training. As result, it is feasible to reduce the costs and the risk of human judgments.

scholarly journals Steel Surface Defect Classification Using Deep Residual Neural Network

Metals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 846
Author(s):  
Ihor Konovalenko ◽  
Pavlo Maruschak ◽  
Janette Brezinová ◽  
Ján Viňáš ◽  
Jakub Brezina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Speed ◽  
Steel Surface ◽  
Surface Defect ◽  
Surface Defects ◽  
Defect Classification ◽  
Flat Surfaces ◽  
Automated Method ◽  
Speed And Accuracy

An automated method for detecting and classifying three classes of surface defects in rolled metal has been developed, which allows for conducting defectoscopy with specified parameters of efficiency and speed. The possibility of using the residual neural networks for classifying defects has been investigated. The classifier based on the ResNet50 neural network is accepted as a basis. The model allows classifying images of flat surfaces with damage of three classes with the general accuracy of 96.91% based on the test data. The use of ResNet50 is shown to provide excellent recognition, high speed, and accuracy, which makes it an effective tool for detecting defects on metal surfaces.

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