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

scholarly journals Modeling the stress state of steel strip at roller levelling machine under cyclic alternating deformations

2020 ◽  
Vol 63 (10) ◽  
pp. 808-814
Author(s):  
K. A. Kotov ◽  
N. L. Bolobanova ◽  
D. V. Nushtaev
Keyword(s):  
Residual Stresses ◽  
Steel Strip ◽  
Lower Surface ◽  
Rolled Strip ◽  
Rolled Steel ◽  
Element Analysis ◽  
Strip Flatness ◽  
Hot Rolled ◽  
Hot Rolled Steel ◽  
Minimal Residual

The final stage in the production of hot rolled steel is leveling on roller levellers under cyclic alternating deformation. When laser is cutting a sheet it may bend due to the release of residual stresses that are unevenly distributed over the volume. The majority of roller leveller models for calculating the process under cyclic alternating deformation does not provide an adequate assessment and prediction of residual stresses in a steel sheet. On the basis of finite element analysis, formation of residual stresses owing to roller levelling of hot rolled strip is disclosed. The implementation of a model of the levelling process was performed in SIMULIA Abaqus. Models are verificated by comparing forces under the rollers. We have experimentally confirmed the convergence of the simulation results with the measurements of the strip flatness obtained after sheets plasma cutting. It was found that after levelling, tensile longitudinal residual stresses remain on the upper surface of the sheet, compressive ones remain on the lower surface, stresses are zero in the middle in thickness, and the stress values are opposite in sign in the remaining parts of the section. It was established that the same parameters of the levelling process of different strength categories lead to different deviations of stresses. An increase in yield strength of the strip leads to an increase in the deviation of residual stresses along the strip thickness. The proposed method of simulation of roller levelling process should be used to study the stress-strain state of hot-rolled steel and to design improved strip levelling setting modes with minimal residual stress deviations.

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.

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