An Integrated Deep Neural Network for Defect Detection in Dynamic Textile Textures

2018 ◽  
pp. 77-84 ◽  
Author(s):  
Dirk Siegmund ◽  
Ashok Prajapati ◽  
Florian Kirchbuchner ◽  
Arjan Kuijper
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Deep Neural Network

A Weld Defect Detection Method Based on Triplet Deep Neural Network

2020 ◽  
Author(s):  
Xiaoyuan Liu ◽  
Jinhai Liu ◽  
Fuming Qu ◽  
Hongfei Zhu ◽  
Danyu Lu
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Deep Neural Network ◽  
Detection Method ◽  
Weld Defect

Defect detection of nuclear fuel assembly based on deep neural network

2020 ◽  
Vol 137 ◽  
pp. 107078 ◽  
Author(s):  
Zhangpeng Guo ◽  
Zhiwang Wu ◽  
Sheng Liu ◽  
Xuan Ma ◽  
Chaoyi Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuel Assembly ◽  
Nuclear Fuel ◽  
Defect Detection ◽  
Deep Neural Network ◽  
Nuclear Fuel Assembly

FFCNN: A Deep Neural Network for Surface Defect Detection of Magnetic Tile

2021 ◽  
Vol 68 (4) ◽  
pp. 3506-3516
Author(s):  
Luofeng Xie ◽  
Xiao Xiang ◽  
Huining Xu ◽  
Ling Wang ◽  
Lijun Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Deep Neural Network ◽  
Surface Defect ◽  
Surface Defect Detection

Defect Detection of Axle Box Cover Device Fixing Bolts in Metro Based on Convolutional Neural Network

2020 ◽  
Author(s):  
Yao Yang ◽  
Yuanjiang Hu ◽  
Lingling Chen ◽  
Xiaoman Liu ◽  
Na Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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