Copper Strip Surface Defects Inspection Based on SVM-RBF

2008 ◽  
Author(s):  
Ruiyu Liang ◽  
Yanqiong Ding ◽  
Xuewu Zhang ◽  
Jiasheng Chen
Keyword(s):  
Surface Defects ◽  
Copper Strip ◽  
Strip Surface ◽  
Defects Inspection

scholarly journals Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network

2021 ◽  
Vol 11 (19) ◽  
pp. 8945
Author(s):  
Yanghuan Xu ◽  
Dongcheng Wang ◽  
Bowei Duan ◽  
Huaxin Yu ◽  
Hongmin Liu
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Surface Defect ◽  
Surface Defects ◽  
Learning Strategy ◽  
Confusion Matrix ◽  
Automatic Detection ◽  
Future Research ◽  
Copper Strip ◽  
Strip Surface

Surface defect automatic detection has great significance for copper strip production. The traditional machine vision for surface defect automatic detection of copper strip needs artificial feature design, which has a long cycle, and poor ability of versatility and robustness. However, deep learning can effectively solve these problems. Therefore, based on the deep convolution neural network and the transfer learning strategy, an intelligent recognition model of surface defects of copper strip is established in this paper. Firstly, the defects were classified in accordance with the mechanism and morphology, and the surface defect dataset of copper strip was established by comprehensively adopting image acquisition and image augmentation. Then, a two-class discrimination model was established to achieve the accurate discrimination of perfect and defect images. On this basis, four CNN models were adopted for the recognition of defect images. Among these models, the EfficientNet model through transfer learning strategy had the best comprehensive performance with a recognition accuracy rate of 93.05%. Finally, the interpretability and deficiency of the model were analysed by the class activation map and confusion matrix, which point toward the direction of further optimization for future research.

scholarly journals X-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 706
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Rolled Strip ◽  
Rolled Steel ◽  
Strip Surface ◽  
Hot Rolled Strip ◽  
Hot Rolled ◽  
Hot Rolled Steel ◽  
Detection Of Defects

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.

scholarly journals An improved Vision Transformer-based method for classifying surface defects in hot-rolled strip steel

2021 ◽  
Vol 2082 (1) ◽  
pp. 012016
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Learning Ability ◽  
Rolled Strip ◽  
Strip Surface ◽  
Strip Steel ◽  
Hot Rolled Strip ◽  
Hot Rolled ◽  
Improved Algorithm

Abstract A new Vision Transformer(ViT) model is proposed for the classification of surface defects in hot rolled strip, optimizing the poor learning ability of the original Vision Transformer model on smaller datasets. Firstly, each module of ViT and its characteristics are analyzed; Secondly, inspired by the deep learning model VGGNet, the multilayer fully connected layer in VGGNet is introduced into the ViT model to increase its learning capability; Finally, by performing on the X-SDD hot-rolled steel strip surface defect dataset. The effect of the improved algorithm is verified by comparison experiments on the X-SDD hot-rolled strip steel surface defect dataset. The test results show that the improved algorithm achieves better results than the original model in terms of accuracy, recall, F1 score, etc. Among them, the accuracy of the improved algorithm on the test set is 5.64% higher than ViT-Base and 2.64% higher than ViT-Huge; the accuracy is 4.68% and 1.36% higher than both of them, respectively.

Inspection of Surface Defects in Copper Strip Based on Machine Vision

2010 ◽  
pp. 304-312 ◽  
Author(s):  
Xue-Wu Zhang ◽  
Li-Zhong Xu ◽  
Yan-Qiong Ding ◽  
Xin-Nan Fan ◽  
Li-Ping Gu ◽  
...  
Keyword(s):  
Machine Vision ◽  
Surface Defects ◽  
Copper Strip

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 Real-time Detection of Steel Strip Surface Defects Based on Improved YOLO Detection Network

2018 ◽  
Vol 51 (21) ◽  
pp. 76-81 ◽  
Author(s):  
Jiangyun Li ◽  
Zhenfeng Su ◽  
Jiahui Geng ◽  
Yixin Yin
Keyword(s):  
Real Time ◽  
Surface Defects ◽  
Steel Strip ◽  
Strip Surface ◽  
Real Time Detection

Simulation of the Mechanism of Insect Bionic Compound Eye Super-Resolution Restoration Technology of Copper Strip Surface Defect Images

2013 ◽  
Vol 462-463 ◽  
pp. 302-307
Author(s):  
Xiao Dan Sun ◽  
Xin Nan Fan ◽  
Ming Qiang Ling ◽  
Hong Hai Zhuang ◽  
Zhuo Zhang ◽  
...  
Keyword(s):  
Surface Defect ◽  
Compound Eye ◽  
Super Resolution ◽  
Reconstruction Technique ◽  
Copper Strip ◽  
Strip Surface ◽  
Imaging Technology ◽  
Inspection Process ◽  
Defect Image ◽  
Surface Defect Detection

In view of the problem that in traditional copper strip surface defect inspection process, the low resolution of the collected images will greatly decrease the accuracy of the detect defects, through the study of biological bionic imaging technology, combined with insect bionic compound eye imaging technology, this paper proposes a visual simulation insects bionic mechanism of the copper strip surface defect image super-resolution reconstruction technique. Through the study of biological bionic imaging technology, and take the advantage of insect compound eye visual imaging mechanism, this paper uses multiple linear array CCD image sensors to collect images aimed at getting defect images in all perspectives. The actual input images are restored by the method of super-resolution reconstruction using the sample library to improve the resolution of the image. Through the large amount of experiments of different copper strip surface defect images, and then compare the results, it can be seen that taking this papers method to dispose the defect images, will improve the images PSNR value, and has greatly improved the images quality, which will do good to improve the accuracy of the copper strip surface defect detection.

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