Steel Surface Defect Detection using Deep Learning

Fadli, Vira Fitriza; Herlistiono, Iwa Ovyawan

doi:10.38124/ijisrt20jul240

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…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: 96%

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

Wang

Xia

et al. 2021

Metals

120

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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.

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Section: The Results Of the Classification Modelmentioning

confidence: 96%

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

Wang

Xia

et al. 2021

Metals

120

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“…Litvintseva et al [133] compared E-Net, DeepLabV3, and U-Net models for metal surface defect recognition, with DeepLabV3 achieving the highest accuracy. Fadli et al [134] employed VGG-16 and VGG-19 models for image recognition of steel surface defects, achieving performance values of 97.20% and 93.30%, respectively. Gao et al [135] proposed a lightweight inspection network for multi-class steel plate surface defect detection.…”

Section: Supervisedmentioning

confidence: 99%

A Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products

Ibrahim,

Tapamo

2024

Informatics

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In the competitive landscape of steel-strip production, ensuring the high quality of steel surfaces is paramount. Traditionally, human visual inspection has been the primary method for detecting defects, but it suffers from limitations such as reliability, cost, processing time, and accuracy. Visual inspection technologies, particularly automation techniques, have been introduced to address these shortcomings. This paper conducts a thorough survey examining vision-based methodologies related to detecting and classifying surface defects on steel products. These methodologies encompass statistical, spectral, texture segmentation based methods, and machine learning-driven approaches. Furthermore, various classification algorithms, categorized into supervised, semi-supervised, and unsupervised techniques, are discussed. Additionally, the paper outlines the future direction of research focus.

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“…The InceptionV3-LR pipeline performed the best on both training and test data compared to the other methods investigated. Another study addressed common steel surface defects like scratches, pitting, inclusions, and patches to separate defective from non-defective surfaces [11]. The authors used a CNN with Xception architecture to detect these defects.…”

Section: Introductionmentioning

confidence: 99%

Surface Defect Detection: A feature-based transfer learning approach

Abdul Majeed,

Abdullah,

Ab. Nasir

et al. 2024

J. Phys.: Conf. Ser.

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Surface defect detection is critical for maintaining product quality in manufacturing. In this work, we apply a feature-based transfer learning approach for surface defect classification on the NEU surface defect database. The database contains defects across 6 categories captured under various conditions. We utilised two pretrained convolutional neural network (CNN) architectures - VGG16 and InceptionV3 - by removing the final classification layer and using the CNN as a fixed feature extractor. The output feature vectors were classified using a logistic regression (LR) model. The data was split into train, validation, and test sets with a 70:15:15 ratio. The VGG16-LR model achieved classification accuracy (CA) of 100%, 98%, and 99% for the train, validation, and test sets respectively. The InceptionV3-LR model attained CA of 100%, 91%, and 92% for train, validation, and test. The results demonstrate the effectiveness of transfer learning with CNN feature extraction for surface defect detection on challenging multi-category industrial datasets. Further work includes tuning hyperparameters and evaluating additional architectures.

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Steel Surface Defect Detection using Deep Learning

Cited by 14 publications

References 8 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 Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products

Surface Defect Detection: A feature-based transfer learning approach

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