scholarly journals Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7264
Author(s):  
Qiwu Luo ◽  
Weiqiang Jiang ◽  
Jiaojiao Su ◽  
Jiaqiu Ai ◽  
Chunhua Yang
Keyword(s):  
Surface Defects ◽  
Steel Strip ◽  
Large Area ◽  
Strip Surface ◽  
Steel Strips ◽  
Surface Image ◽  
High Resolution Images ◽  
Feature Pyramid ◽  
Hot Rolled ◽  
Hot Rolled Steel

Steel strip acts as a fundamental material for the steel industry. Surface defects threaten the steel quality and cause substantial economic and reputation losses. Roll marks, always occurring periodically in a large area, are put on the top of the list of the most serious defects by steel mills. Essentially, the online roll mark detection is a tiny target inspection task in high-resolution images captured under harsh environment. In this paper, a novel method—namely, Smoothing Complete Feature Pyramid Networks (SCFPN)—is proposed for the above focused task. In particular, the concept of complete intersection over union (CIoU) is applied in feature pyramid networks to obtain faster fitting speed and higher prediction accuracy by suppressing the vanishing gradient in training process. Furthermore, label smoothing is employed to promote the generalization ability of model. In view of lack of public surface image database of steel strips, a raw defect database of hot-rolled steel strip surface, CSU_STEEL, is opened for the first time. Experiments on two public databases (DeepPCB and NEU) and one fresh texture database (CSU_STEEL) indicate that our SCFPN yields more competitive results than several prestigious networks—including Faster R-CNN, SSD, YOLOv3, YOLOv4, FPN, DIN, DDN, and CFPN.

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.

Texture Descriptors for Automatic Classification of Surface Defects of the Hot-Rolled Steel Strip

2021 ◽  
pp. 251-260
Author(s):  
Virginia Riego del Castillo ◽  
Lidia Sánchez-González ◽  
Alexis Gutiérrez-Fernández
Keyword(s):  
Surface Defects ◽  
Steel Strip ◽  
Automatic Classification ◽  
Rolled Steel ◽  
Hot Rolled ◽  
Hot Rolled Steel

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

Study of the Development of Heterogeneous Grain Size in the Through-Thickness Direction of Hot Rolled ELC Steels

2007 ◽  
Vol 539-543 ◽  
pp. 4220-4225
Author(s):  
Frans Leysen ◽  
Jan Penning ◽  
Yvan Houbaert
Keyword(s):  
Penetration Depth ◽  
Texture Analysis ◽  
Grain Growth ◽  
Hot Rolling ◽  
Steel Strip ◽  
Abnormal Grain Growth ◽  
Thickness Direction ◽  
Strip Surface ◽  
Steel Strips ◽  
Hot Rolled

The present study aims to investigate the mechanism of the development of abnormal grain sizes in the through-thickness direction of hot rolled steel strips. For this purpose, industrially prepared steel strips were further hot rolled in a laboratory hot rolling mill, setting a variety of rolling parameters. As found, the deformation rate in the hot rolling practice exerts an important role in explaining the mechanism of abnormal grain growth, especially in the close vicinity of the strip surface. Furthermore, the influence of the cooling penetration depth, induced by the roll contact was examined closely, as this phenomenon might support abnormal grain growth mechanisms. Additional information was found in performing a texture analysis in the throughthickness direction of the steel strips, in accordance with the optical metallurgical survey of the microstructures. It will be shown that, the combination of particular hot rolling parameters provokes the occurrence of abnormal grain growth in the through-thickness direction of the ELC steel strips. These particular conditions were considered to be related to the finish hot rolling temperature and thus the roll cooling penetration depth imposed on the steel strip, the finishing reduction degree and especially the strain rate conditions. Moreover, the observed abnormal grain growth is sensitive to the coiling temperature applied. From the experiments, it can be concluded that the mechanism of the formation of a large grained ferrite band below the strip surface is strongly influenced by the development of a fine-grain ferrite layer at some distance below the strip surface. The existence of this layer of very small ferrite grains can be explained on the basis of texture analysis and calculations based on literature data. In this way, it was considered that dynamic recrystallisation of austenite at some depth below the steel strip surface is of most significance in supporting the development of abnormally large ferrite grains. In this paper, further considerations on the mechanism of the abnormal grain growth phenomenon will be dealt with.

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