Steel Surface Defect Classification Using Deep Residual Neural Network

Konovalenko, І. V.; Maruschak, Pavlo; Brezinová, Janette; Viňáš, Ján; Brezina, Jakub

doi:10.3390/met10060846

Cited by 72 publications

(30 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the classification algorithm, we compare ResNet and ResNet _ vd, ResNet_ vd_ dcnv2, ResNet_ vd_ dcnv2_ ImprovedCutout, Fadli et al [43], and konovalenko et al [44]. We can find that the improved method has better performance, and the highest accuracy can reach 0.9752.…”

Section: The Results Of the Classification Modelmentioning

confidence: 95%

Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks

Wang

Xia

et al. 2021

Metals

View full text Add to dashboard Cite

Automatic detection of steel surface defects is very important for product quality control in the steel industry. However, the traditional method cannot be well applied in the production line, because of its low accuracy and slow running speed. The current, popular algorithm (based on deep learning) also has the problem of low accuracy, and there is still a lot of room for improvement. This paper proposes a method combining improved ResNet50 and enhanced faster region convolutional neural networks (faster R-CNN) to reduce the average running time and improve the accuracy. Firstly, the image input into the improved ResNet50 model, which add the deformable revolution network (DCN) and improved cutout to classify the sample with defects and without defects. If the probability of having a defect is less than 0.3, the algorithm directly outputs the sample without defects. Otherwise, the samples are further input into the improved faster R-CNN, which adds spatial pyramid pooling (SPP), enhanced feature pyramid networks (FPN), and matrix NMS. The final output is the location and classification of the defect in the sample or without defect in the sample. By analyzing the data set obtained in the real factory environment, the accuracy of this method can reach 98.2%. At the same time, the average running time is faster than other models.

show abstract

Section: The Results Of the Classification Modelmentioning

confidence: 95%

Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks

Wang

Xia

et al. 2021

Metals

View full text Add to dashboard Cite

show abstract

“…The experimental results confirm that their proposed method is quite simple, effective and robust for the classification of surface defects in hot rolled steel sheets. Konovalenko I et al [17] used a deep learning model based on ResNet50 as the base classifier to perform classification experiments on planar images with three types of damage, and the results showed that the model has excellent recognition ability, high speed and accuracy at the same time. Yi et al [18] proposed an end-to-end surface defect recognition system for steel strip surface inspection.…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

A ResNet50-Based Method for Classifying Surface Defects in Hot-Rolled Strip Steel

Feng

Gao

Luo³

2021

Mathematics

View full text Add to dashboard Cite

Hot-rolled strip steel is widely used in automotive manufacturing, chemical and home appliance industries, and its surface quality has a great impact on the quality of the final product. In the manufacturing process of strip steel, due to the rolling process and many other reasons, the surface of hot rolled strip steel will inevitably produce slag, scratches and other surface defects. These defects not only affect the quality of the product, but may even lead to broken strips in the subsequent process, seriously affecting the continuation of production. Therefore, it is important to study the surface defects of strip steel and identify the types of defects in strip steel. In this paper, a scheme based on ResNet50 with the addition of FcaNet and Convolutional Block Attention Module (CBAM) is proposed for strip defect classification and validated on the X-SDD strip defect dataset. Our solution achieves a classification accuracy of 94.11%, higher than more than a dozen other compared deep learning models. Moreover, to adress the problem of low accuracy of the algorithm in classifying individual defects, we use ensemble learning to optimize. By integrating the original solution with VGG16 and SqueezeNet, the recognition rate of oxide scale of plate system defects improved by 21.05 percentage points, and the overall defect classification accuracy improved to 94.85%.

show abstract

“…Such systems are based on a previous study of defect groups using materials from databases, technological and morphological analysis [8]. In previous articles, the types and causes of the main defects are analyzed, and their characteristics are described [9]. However, even within one class, defects may have significant differences in shape.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Scratches and Abrasions on Metal Surfaces Using a Classifier Based on a Convolutional Neural Network

Konovalenko

Maruschak

Brevus³

et al. 2021

Metals

Self Cite

View full text Add to dashboard Cite

Classification of steel surface defects in steel industry is essential for their detection and also fundamental for the analysis of causes that lead to damages. Timely detection of defects allows to reduce the frequency of their appearance in the final product. This paper considers the classifiers for the recognition of scratches, scrapes and abrasions on metal surfaces. Classifiers are based on the ResNet50 and ResNet152 deep residual neural network architecture. The proposed technique supports the recognition of defects in images and does this with high accuracy. The binary accuracy of the classification based on the test data is 97.14%. The influence of a number of training conditions on the accuracy metrics of the model have been studied. The augmentation conditions have been figured out to make the greatest contribution to improving the accuracy during training. The peculiarities of damages that cause difficulties in their recognition have been studied. The fields of neuron activation have been investigated in the convolutional layers of the model. Feature maps which developed in this case have been found to correspond to the location of the objects of interest. Erroneous cases of the classifier application have been considered. The peculiarities of damages that cause difficulties in their recognition have been studied.

show abstract

Steel Surface Defect Classification Using Deep Residual Neural Network

Cited by 72 publications

References 32 publications

Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks

Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks

A ResNet50-Based Method for Classifying Surface Defects in Hot-Rolled Strip Steel

Recognition of Scratches and Abrasions on Metal Surfaces Using a Classifier Based on a Convolutional Neural Network

Contact Info

Product

Resources

About