Automatic surface defects classification of Kinnow mandarins using combination of multi‐feature fusion techniques

2020 ◽  
Vol 44 (1) ◽  
Author(s):  
Lingaraj Hadimani ◽  
Neerja Mittal Garg
Keyword(s):  
Surface Defects ◽  
Feature Fusion ◽  
Defects Classification

Surface Defects Classification of Hot-Rolled Steel Strips Using Multi-directional Shearlet Features

2018 ◽  
Vol 44 (4) ◽  
pp. 2925-2932 ◽  
Author(s):  
Mohammed Waleed Ashour ◽  
Fatimah Khalid ◽  
Alfian Abdul Halin ◽  
Lili Nurliyana Abdullah ◽  
Samy Hassan Darwish
Keyword(s):  
Surface Defects ◽  
Rolled Steel ◽  
Steel Strips ◽  
Hot Rolled ◽  
Hot Rolled Steel ◽  
Defects Classification

scholarly journals Surface Defects Classification of Hot Rolled Strip Based on Improved Convolutional Neural Network

2021 ◽  
Vol 61 (5) ◽  
pp. 1579-1583
Author(s):  
Wenyan Wang ◽  
Kun Lu ◽  
Ziheng Wu ◽  
Hongming Long ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Surface Defects ◽  
Rolled Strip ◽  
Hot Rolled Strip ◽  
Hot Rolled ◽  
Defects Classification

Steel Surface Defect Detection Using an Ensemble of Deep Residual Neural Networks

2021 ◽  
pp. 1-8
Author(s):  
Ihor Konovalenko ◽  
Pavlo Maruschak ◽  
Vitaly Brevus
Keyword(s):  
Neural Networks ◽  
Steel Surface ◽  
Production Efficiency ◽  
Surface Defects ◽  
Model Parameters ◽  
Feature Maps ◽  
Defect Diagnostics ◽  
Neuron Activation ◽  
Surface Defect Detection

Abstract Steel defect diagnostics is important for industry task as it is tied to the product quality and production efficiency. The aim of this paper is evaluating the application of residual neural networks for recognition of industrial steel defects of three classes. Developed and investigated models based on deep residual neural networks for the recognition and classification of surface defects of rolled steel. Investigated the influence of various loss functions, optimizers and hyperparameters on the obtained result and selected optimal model parameters. Based on an ensemble of two deep residual neural networks ResNet50 and ResNet152, a classifier was constructed to detect defects of three classes on flat metal surfaces. The proposed technique allows classifying images with high accuracy. The average binary accuracy of classifying the test data is 96.7% for all images (including defect-free ones). The fields of neuron activation in the convolutional layers of the model were investigated. Feature maps formed in the process were found to reflect the position, size and shape of the objects of interest very well. The proposed ensemble model has proven to be robust and able to accurately recognize steel surface defects. Erroneous recognition cases of the classifier application are investigated. It was shown that errors most often occur in ambiguous situations, where surface artifacts of different types are similar.

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