scholarly journals Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

2019 ◽  
Vol 68 (3) ◽  
pp. 667-679 ◽  
Author(s):  
Qiwu Luo ◽  
Yichuang Sun ◽  
Pengcheng Li ◽  
Oluyomi Simpson ◽  
Lu Tian ◽  
...  
Keyword(s):  
Steel Surface ◽  
Surface Defect ◽  
Local Binary Patterns ◽  
Defect Classification

scholarly journals Steel Surface Defect Classification Based on Small Sample Learning

2021 ◽  
Vol 11 (23) ◽  
pp. 11459
Author(s):  
Shiqing Wu ◽  
Shiyu Zhao ◽  
Qianqian Zhang ◽  
Long Chen ◽  
Chenrui Wu
Keyword(s):  
Feature Extraction ◽  
Steel Surface ◽  
Surface Defect ◽  
Nearest Neighbor ◽  
Surface Defects ◽  
Feature Space ◽  
Small Sample ◽  
Defect Classification ◽  
Sample Number ◽  
Degradation Problem

The classification of steel surface defects plays a very important role in analyzing their causes to improve manufacturing process and eliminate defects. However, defective samples are very scarce in actual production, so using very few samples to construct a good classifier is a challenge to be addressed. If the layer number of the model with proper depth is increased, the model accuracy will decrease (not caused by overfit), and the training error as well as the test error will be very high. This is called the degradation problem. In this paper, we propose to use feature extraction + feature transformation + nearest neighbors to classify steel surface defects. In order to solve the degradation problem caused by network deepening, the three feature extraction networks of Residual Net, Mobile Net and Dense Net are designed and analyzed. Experiment results show that in the case of a small sample number, Dense block can better solve the degradation problem caused by network deepening than Residual block. Moreover, if Dense Net is used as the feature extraction network, and the nearest neighbor classification algorithm based on Euclidean metric is used in the new feature space, the defect classification accuracy can reach 92.33% when only five labeled images of each category are used as the training set. This paper is of some guiding significance for surface defect classification when the sample number is small.

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.

Steel surface defect classification using multi-resolution empirical mode decomposition and LBP

2020 ◽  
Vol 32 (1) ◽  
pp. 015601
Author(s):  
Siti Salbiah Samsudin ◽  
Hamzah Arof ◽  
Sulaiman Wadi Harun ◽  
Ainuddin Wahid Abdul Wahab ◽  
Mohamad Yamani Idna Idris
Keyword(s):  
Empirical Mode Decomposition ◽  
Steel Surface ◽  
Surface Defect ◽  
Defect Classification ◽  
Mode Decomposition

scholarly journals A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification

Materials ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 4629
Author(s):  
Yang Liu ◽  
Yachao Yuan ◽  
Cristhian Balta ◽  
Jing Liu
Keyword(s):  
Real Time ◽  
Steel Surface ◽  
Surface Defect ◽  
Surface Defects ◽  
Defect Classification ◽  
Multi Scale ◽  
Training Samples ◽  
Training Cost ◽  
Real Time Applications ◽  
Deep Learning Model

Automatic inspection of surface defects is crucial in industries for real-time applications. Nowadays, computer vision-based approaches have been successfully employed. However, most of the existing works need a large number of training samples to achieve satisfactory classification results, while collecting massive training datasets is labor-intensive and financially costly. Moreover, most of them obtain high accuracy at the expense of high latency, and are thus not suitable for real-time applications. In this work, a novel Concurrent Convolutional Neural Network (ConCNN) with different image scales is proposed, which is light-weighted and easy to deploy for real-time defect classification applications. To evaluate the performance of ConCNN, the NEU-CLS dataset is used in our experiments. Simulation results demonstrate that ConCNN performs better than other state-of-the-art approaches considering accuracy and latency for steel surface defect classification. Specifically, ConCNN achieves as high as 98.89% classification accuracy with only around 5.58 ms latency over low training cost.

scholarly journals Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 23488-23499 ◽  
Author(s):  
Qiwu Luo ◽  
Xiaoxin Fang ◽  
Yichuang Sun ◽  
Li Liu ◽  
Jiaqiu Ai ◽  
...  
Keyword(s):  
Surface Defect ◽  
Local Binary Patterns ◽  
Defect Classification ◽  
Rolled Steel ◽  
Steel Strips ◽  
Hot Rolled ◽  
Hot Rolled Steel

scholarly journals A Multi-Branch U-Net for Steel Surface Defect Type and Severity Segmentation

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 870
Author(s):  
Robby Neven ◽  
Toon Goedemé
Keyword(s):  
Process Parameters ◽  
Visual Inspection ◽  
Steel Surface ◽  
Surface Defect ◽  
State Of The Art ◽  
Sheet Steel ◽  
Real Life ◽  
Single Task ◽  
Severity Estimation ◽  
Task Model

Automating sheet steel visual inspection can improve quality and reduce costs during its production. While many manufacturers still rely on manual or traditional inspection methods, deep learning-based approaches have proven their efficiency. In this paper, we go beyond the state-of-the-art in this domain by proposing a multi-task model that performs both pixel-based defect segmentation and severity estimation of the defects in one two-branch network. Additionally, we show how incorporation of the production process parameters improves the model’s performance. After manually constructing a real-life industrial dataset, we first implemented and trained two single-task models performing the defect segmentation and severity estimation tasks separately. Next, we compared this to a multi-task model that simultaneously performs the two tasks at hand. By combining the tasks into one model, both segmentation tasks improved by 2.5% and 3% mIoU, respectively. In the next step, we extended the multi-task model using sensor fusion with process parameters. We demonstrate that the incorporation of the process parameters resulted in a further mIoU increase of 6.8% and 2.9% for the defect segmentation and severity estimation tasks, respectively.

Sign in / Sign up

Export Citation Format

Share Document