Defect Detection of Steel Surfaces with Global Adaptive Percentile Thresholding of Gradient Image

Neogi, Nirbhar; Mohanta, Dusmanta Kumar; Dutta, Pranab

doi:10.1007/s40031-017-0296-2

Cited by 47 publications

(27 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…low-quality steel surface than former dynamical thresholding method. To obtain a better global detection performance, Neogi et al [23] proposed a global adaptive percentile thresholding scheme based on gradient images. It can selectively segment defective region and effectively preserve the defect edges regardless of the size of defects.…”

Section: ) Thresholdingmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

340

111

View full text Add to dashboard Cite

Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot-and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

show abstract

Section: ) Thresholdingmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

340

111

View full text Add to dashboard Cite

show abstract

“…Statistical-based: This type of methods is generally based on the evaluation of the regular and periodic distributions of pixel intensities. Neogi et al [ 8 ] considered a global dynamic percentile thresholding method over gradient images for various kinds of defects on steel strips. The adaptive threshold changes according to the number of the pixels on the specific region of gradient images.…”

Section: Related Workmentioning

confidence: 99%

A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification

et al. 2020

View full text Add to dashboard Cite

Automatic inspection of surface defects is crucial in industries for real-time applications. Nowadays, computer vision-based approaches have been successfully employed. However, most of the existing works need a large number of training samples to achieve satisfactory classification results, while collecting massive training datasets is labor-intensive and financially costly. Moreover, most of them obtain high accuracy at the expense of high latency, and are thus not suitable for real-time applications. In this work, a novel Concurrent Convolutional Neural Network (ConCNN) with different image scales is proposed, which is light-weighted and easy to deploy for real-time defect classification applications. To evaluate the performance of ConCNN, the NEU-CLS dataset is used in our experiments. Simulation results demonstrate that ConCNN performs better than other state-of-the-art approaches considering accuracy and latency for steel surface defect classification. Specifically, ConCNN achieves as high as 98.89% classification accuracy with only around 5.58 ms latency over low training cost.

show abstract

“…Under normal circumstances, the gradient image can robustly reflect the image structures in details under the variations of the image intensities and colors, and the defect area gradient information is more sensitive to defect classification and recognition [21]. We believe that using the two-stream feature fusion based on the gradient image stream and the original RGB image stream will promote the detection performance.…”

Section: B Gradient Image Of the Original Grb Imagementioning

confidence: 99%

Two-Stream Convolutional Neural Network Based on Gradient Image for Aluminum Profile Surface Defects Classification and Recognition

Duan

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

In this paper, a novel two-stream convolutional neural network based on gradient image is performed to effectively classify and identify aluminum profiles defects for the first time. Recent feature fusion methods based on two-stream network prove promising performance for defects classification and recognition. In this paper, we use data enhancement methods to obtain a large number of samples to prevent the over fitting phenomenon in deep learning. The image gradient is calculated with the Sobel operator, and normalized to transform the data between zero and one under the same dimension. We design a two-stream convolutional neural network model adopting Wavelet transform fusion strategy to realize feature fusion on the ReLU6 layer, which uses the original RGB image of aluminum profile and the gradient image corresponding to the original RGB image as inputs to extract features through two sub-networks and fuses features on a concatenate layer to be input into SVM classifier for classification and recognition. Using Bayesian Optimization function and computing the cross-validation classification error to optimize the hyperparameters to choose the best performance configuration is performed. A series of experimental data, which include accuracy and estimated generalized classification errors of single-stream and two-stream networks with different feature fusion strategies on different fusion layers, are conducted and show that the current model has good convergence, accuracy, stability and generalization. On this basis, this paper also proposes a series of innovative methods for the future research of other defects. INDEX TERMS Aluminum profile surface defects, two-stream network, gradient image, convolutional neural network, SVM.

show abstract

Defect Detection of Steel Surfaces with Global Adaptive Percentile Thresholding of Gradient Image

Cited by 47 publications

References 7 publications

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Automated Visual Defect Detection for Flat Steel Surface: A Survey

A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification

Two-Stream Convolutional Neural Network Based on Gradient Image for Aluminum Profile Surface Defects Classification and Recognition

Contact Info

Product

Resources

About