Review of Vision Real-Time Inspection Algorithm for Rolling Steel Surface Defects

2011 ◽  
Vol 308-310 ◽  
pp. 1328-1332 ◽  
Author(s):  
Wu Bin Li ◽  
Chang Hou Lu ◽  
Jian Chuan Zhang
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Steel Surface ◽  
Surface Defect ◽  
Surface Defects ◽  
Defect Inspection ◽  
Development Trends ◽  
Key Issues ◽  
At Home

Rolling steel surface defect inspection technology based on machine vision is more and more widely used. The latest progress of vision-based real-time inspection algorithm for rolling steel surface defect at home and abroad is introduced, and several key issues are analyzed. Finally, the current domestic research emphases and development trends are proposed.

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 Impact of Image Compression on the Performance of Steel Surface Defect Classification with a CNN

2021 ◽  
Vol 10 (4) ◽  
pp. 73
Author(s):  
Tajeddine Benbarrad ◽  
Lamiae Eloutouate ◽  
Mounir Arioua ◽  
Fatiha Elouaai ◽  
My Driss Laanaoui
Keyword(s):  
Machine Vision ◽  
Image Compression ◽  
High Speed ◽  
Steel Surface ◽  
Surface Defect ◽  
Surface Defects ◽  
High Accuracy ◽  
Classification Performance ◽  
Inspection Time ◽  
The Impact

Machine vision is increasingly replacing manual steel surface inspection. The automatic inspection of steel surface defects makes it possible to ensure the quality of products in the steel industry with high accuracy. However, the optimization of inspection time presents a great challenge for the integration of machine vision in high-speed production lines. In this context, compressing the collected images before transmission is essential to save bandwidth and energy, and improve the latency of vision applications. The aim of this paper was to study the impact of quality degradation resulting from image compression on the classification performance of steel surface defects with a CNN. Image compression was applied to the Northeastern University (NEU) surface-defect database with various compression ratios. Three different models were trained and tested with these images to classify surface defects using three different approaches. The obtained results showed that trained and tested models on the same compression qualities maintained approximately the same classification performance for all used compression grades. In addition, the findings clearly indicated that the classification efficiency was affected when the training and test datasets were compressed using different parameters. This impact was more obvious when there was a large difference between these compression parameters, and for models that achieved very high accuracy. Finally, it was found that compression-based data augmentation significantly increased the classification precision to perfect scores (98–100%), and thus improved the generalization of models when tested on different compression qualities. The importance of this work lies in exploiting the obtained results to successfully integrate image compression into machine vision systems, and as appropriately as possible.

scholarly journals SurfNetv2: An Improved Real-Time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4356 ◽  
Author(s):  
Chi-Yi Tsai ◽  
Hao-Wei Chen
Keyword(s):  
Real Time ◽  
Calcium Silicate ◽  
Surface Defect ◽  
Surface Defects ◽  
Visual Image ◽  
Training Dataset ◽  
Image Information ◽  
Training Stage ◽  
High Recognition Accuracy ◽  
Defect Recognition

This paper presents an improved Convolutional Neural Network (CNN) architecture to recognize surface defects of the Calcium Silicate Board (CSB) using visual image information based on a deep learning approach. The proposed CNN architecture is inspired by the existing SurfNet architecture and is named SurfNetv2, which comprises a feature extraction module and a surface defect recognition module. The output of the system is the recognized defect category on the surface of the CSB. In the collection of the training dataset, we manually captured the defect images presented on the surface of the CSB samples. Then, we divided these defect images into four categories, which are crash, dirty, uneven, and normal. In the training stage, the proposed SurfNetv2 is trained through an end-to-end supervised learning method, so that the CNN model learns how to recognize surface defects of the CSB only through the RGB image information. Experimental results show that the proposed SurfNetv2 outperforms five state-of-the-art methods and achieves a high recognition accuracy of 99.90% and 99.75% in our private CSB dataset and the public Northeastern University (NEU) dataset, respectively. Moreover, the proposed SurfNetv2 model achieves a real-time computing speed of about 199.38 fps when processing images with a resolution of 128 × 128 pixels. Therefore, the proposed CNN model has great potential for real-time automatic surface defect recognition applications.

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.

scholarly journals Oil seal surface defect detection using superpixel segmentation and circumferential difference

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142097651
Author(s):  
Zhendong He ◽  
Jie Liu ◽  
Liying Jiang ◽  
Suna Zhao ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Vision System ◽  
Small Variation ◽  
Low Contrast ◽  
Superpixel Segmentation ◽  
Surface Image ◽  
Surface Defect Detection ◽  
Key Issues ◽  
Background Difference

Surface defects affect the quality and safety of oil seals. It is a challenge to detect such defects in a vision system because of the unequal reflection property of oil seal surfaces and low contrast between the defect and the background. This article proposes a visual detection method (VDM) for oil seal surface defects and outlines two key issues of VDMs. First, we present a superpixel segmentation algorithm based on the significant gray level variation in the radial direction of an oil seal surface image. This image is then divided into several ring belts. Subsequently, considering the reflection inequality and low contrast, we propose a new circumferential background difference algorithm based on the small variation along the circumferential direction of the image. This algorithm eliminates the influence of the reflection inequality and improves the contrast distinction between the defects and the background. The experimental results verify the effectiveness of the proposed method with a recall and precision as high as 95.2% and 86.8%, respectively.

Simulation of a machine vision system for reflective surface defect inspection based on ray tracing

2020 ◽  
Vol 59 (8) ◽  
pp. 2656
Author(s):  
Pengfei Zhang ◽  
Pin Cao ◽  
Yongying Yang ◽  
Pan Guo ◽  
Shiwei Chen ◽  
...  
Keyword(s):  
Machine Vision ◽  
Ray Tracing ◽  
Surface Defect ◽  
Vision System ◽  
Machine Vision System ◽  
Defect Inspection ◽  
Reflective Surface

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.

Research on On-Line Surface Defect Detection for Steel Strip Based on Sparse Coding

2012 ◽  
Vol 548 ◽  
pp. 749-752 ◽  
Author(s):  
Zhao Liu ◽  
Jia Hu ◽  
Li Hu ◽  
Xiao Long Zhang ◽  
Jian Yi Kong
Keyword(s):  
Machine Vision ◽  
Sparse Coding ◽  
Visual Information ◽  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Feature Representation ◽  
Surface Defect Detection ◽  
On Line ◽  
Object Feature

In the field of metallurgy, surface defects detection for steel plate based on machine vision is a new key technology. In order to improve the accuracy and speed of machine vision in real-time surface defects detection, taking into account the neurons selectivity and sparseness to visual information, we present a flexible data selection mechanism in the layer of photoreceptors and a new sparse coding model for object feature representation and object recognition. Experiments show that the new method is more effective and more effective in the process of training and classification. The key finding of this study is that, the effective sparse coding mechanism not only could have occurred in the data input stage, but also could be in a new way.

A Real-Time Steel Surface Defect Detection Approach with High Accuracy

2021 ◽  
pp. 1-1
Author(s):  
Wenyan Wang ◽  
Chunfeng Mi ◽  
Ziheng Wu ◽  
Kun Lu ◽  
Hongming Long ◽  
...  
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Steel Surface ◽  
Surface Defect ◽  
High Accuracy ◽  
Detection Approach ◽  
Surface Defect Detection
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