Improved cross pattern approach for steel surface defect recognition

Mentouri, Zoheir; Doghmane, Hakim; Moussaoui, Abdelkrim; Bourouba, Hocine

doi:10.1007/s00170-020-06050-x

Cited by 12 publications

(9 citation statements)

References 14 publications

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“…Tian Hongzhi et al designed a micro defect detection system for grinding surface by combining plane illumination mode with multi angle illumination mode [11]. Mentouri zoheir et al employed an improved dual cross algorithm to online monitoring of steel surface quality [12]. Aiming at the problems of complex defect pattern and low contrast between defect and background in steel strip surface defect detection, Liu Kun proposed a total variation image decomposition algorithm based on self-reference template and improved index gradient similarity [13].…”

Section: Related Workmentioning

confidence: 99%

Cylinder Liner Defect Detection and Classification based on Deep Learning

Gao¹,

Fei²,

Song³

et al. 2022

IJACSA

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The machine vision-based defect detection for cylinder liner is a challenging task due to irregular shape, various and small defects on the cylinder liner surface. To improve the accuracy of defect detection by machine vision a deep learning-based defect detection method for cylinder liner was explored in this paper. First, a machine vision system was designed based on the analysis of the causes and types of defects to obtain the field images for establishing an original dataset. Then the dataset was augmented by a modified augmentation method which combines the region of interest automatic extraction method with the traditional augmentation methods. Except for introduction of the anchor configuration optimization method, an XML file-based method of highlighting defect area was proposed to address the problem of tiny defect detection. The optimal model was experimentally determined by considering the network model, the training strategy and the sample size. Finally, the detection system was developed and the network model was deployed. Experiments are carried out and the results of the proposed method compared with those of the traditional methods. The results show that the detection accuracies of sand, scratch and wear defects are 77.5%, 70% and 66.3% which are improved by at least 26.3% compared with the traditional methods. The proposal can be used for field defect detection of cylinder liner.

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Section: Related Workmentioning

confidence: 99%

Cylinder Liner Defect Detection and Classification based on Deep Learning

Gao¹,

Fei²,

Song³

et al. 2022

IJACSA

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“…It encodes discriminatory characteristics of images in the main eight directions. DCP was born for face recognition [21], but recently found applications in the detection of steel surface defects, like in [15].…”

Section: Traditional Texture Descriptorsmentioning

confidence: 99%

“…Histograms of these images are computed and concatenated in the texture descriptor itself. More details on DCP computation can be found in [15,21].…”

Section: Traditional Texture Descriptors: Lbp Dcp Hog Glcmmentioning

confidence: 99%

“…Deep learning represented the natural evolution of machine learning, gaining the capacity to automatically identify discriminatory characteristics of signals and pictures from training data [4,5]. Machine learning firstly, and deep learning, secondly, were involved in many fields related to image processing and classification ranging from land-use categories classification [6], object recognition and classification in road scans [7], to traffic signs recognition [8], texture analysis in turning processes [9] and surface defect recognition [10][11][12][13][14][15], as well as prediction of car prices from images [16], robot assembly operations [16] and manufacturing trajectory smoothing [17].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of milling condition by image processing of the produced surfaces

Carbone

Bernini

Albertelli

et al. 2022

Int J Adv Manuf Technol

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The digital industrial revolution calls for smart manufacturing plants, i.e. plants that include sensors and vision systems accompanied with artificial intelligence and advanced data analytics in order to meet the required accuracy, reliability and productivity levels. In this paper, we introduce a surface analysis and classification approach based on a deep learning algorithm. The approach is intended to let machining centres recognise the adequacy of process parameters adopted for the milling operation performed, based on the phenomenological effects left on the machined surface. Indeed, the operator will be able to understand how to change process parameters to improve workpiece quality of subsequent parts by a reverse engineering procedure that reconstructs the process parameters that generated the analysed surface. A shallow convolutional neural network was proposed to work on surface image patches based on a limited training dataset of optimal and undesired cutting conditions. The architecture consists of a series of 3 stacked convolutional blocks. The performance of the proposed solution was validated through 5-fold cross-validation, measuring the mean and standard deviation of the f1-score metric. The algorithm arrived at outperformed the best state-of-the-art approach by 4.8% when considering average classification performance.

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“…Xiong et al (2020) proposed a glass surface defect detection method based on multi-scale CNNs, which can achieve high recognition accuracy. Mentouri et al (2020) used dual cross pattern approach for steel surface defect recognition, the pixel coding that considers the variations of the intensity in several directions and captures the information from more than one pixel-neighborhood level makes it to better detect the variability in the defect image. Jin et al (2020) proposed an internal crack defect detection method based on the Relief algorithm and Adaboost-SVM, which has a better classification performance and generalization ability than other common classifiers.…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate detection of surface defect based on improved YOLOv4

Lian¹,

He²,

Niu³

et al. 2021

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Purpose The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems. Design/methodology/approach On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects. Findings The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model. Originality/value This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.

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Improved cross pattern approach for steel surface defect recognition

Cited by 12 publications

References 14 publications

Cylinder Liner Defect Detection and Classification based on Deep Learning

Cylinder Liner Defect Detection and Classification based on Deep Learning

Assessment of milling condition by image processing of the produced surfaces

Fast and accurate detection of surface defect based on improved YOLOv4

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