Automated surface defect detection framework using machine vision and convolutional neural networks

Singh, Swarit Anand; Desai, K. A.

doi:10.1007/s10845-021-01878-w

Cited by 110 publications

(32 citation statements)

References 47 publications

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“…Since 2012, the deep learning method has achieved continuous breakthroughs, VGG, GoogleNet, ResNet, and other networks that have been proposed by researchers, and the classification accuracy has been improved, especially the image visualization and semantic analysis algorithms [ 6 , 7 ] continuously improves the deep learning technologies. Many defect detection frameworks using machine vision were applied in practice [ 8 ]. Convolutional neural network (CNN) has gradually become the leading method for recognition and detection tasks, and the deep learning method has also been deeply applied in magnetic tile detection.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Tile Surface Defect Detection Methodology Based on Self-Attention and Self-Supervised Learning

Ling

Ali

et al. 2022

Computational Intelligence and Neuroscience

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As the core component of permanent magnet motor, the magnetic tile defects seriously affect the quality of industrial motor. Automatic recognition of the surface defects of the magnetic tile is a difficult job since the patterns of the defects are complex and diverse. The existing defect recognition methods result in difficulty in practical application due to the complicated system structure and the low accuracy of the image segmentation and the target detection for the diversity of the defect patterns. A self-supervised learning (SSL) method, which benefits from its nonlinear feature extraction performance, is proposed in this study to improve the existing approaches. We proposed an efficient multihead self-attention method, which can automatically locate single or multiple defect areas of magnetic tile and extract features of the magnetic tile defects. We also designed an accurate full-connection classifier, which can accurately classify different defects of magnetic tile defects. A knowledge distillation process without labeling is proposed, which simplifies the self-supervised training process. The process of our method is as follows. A feature extraction model consists of standard vision transformer (ViT) backbone, which is trained by contrast learning without labeled dataset that is used to extract global and local features from the input magnetic tile images. Then, we use a full-connection neural network, which is trained by using labeled dataset to classify the known defect types. Finally, we combined the feature extraction model and defect classification model together to form a relatively simple integrated system. The public magnetic tile surface defect dataset, which holds 5 defect categories and 1 nondefect category, is used in the process of training, validating, and testing. We also use online data augmentation techs to increase training samples to make the model converge and achieve high classification accuracy. The experimental results show that the features extracted by the SSL method can get richer and more detailed features than the supervised learning model gets. The composite model reaches to a high testing accuracy of 98.3%, and gains relatively strong robustness and good generalization ability.

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Section: Introductionmentioning

confidence: 99%

Magnetic Tile Surface Defect Detection Methodology Based on Self-Attention and Self-Supervised Learning

Ling

Ali

et al. 2022

Computational Intelligence and Neuroscience

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“…Chang et al ( 2020 ) proposed a back-propagation Neural Network optimized by Mind Evolutionary Algorithm to predict the penetration quality of asymmetrical fillet root welding. Singh and Desai ( 2022 ) developed an image-based framework considering pre-trained Convolutional Neural Network (CNN), ResNet-101 to detect surface defects on the centerless grinding of tapered rollers. Kusuma and Huang ( 2022 ) predicted the product quality of silicon steel sheets in terms of the average kerf width of a straight slot cutting by using the DNN.…”

Section: Related Workmentioning

confidence: 99%

Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application

et al. 2023

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Machine learning with considering data privacy-preservation and personalized models has received attentions, especially in the manufacturing field. The data often exist in the form of isolated islands and cannot be shared because of data privacy in real industrial scenarios. It is difficult to gather the data to train a personalized model without compromising data privacy. To address this issue, we proposed a Federated Transfer Learning framework based on Auxiliary Classifier Generative Adversarial Networks named ACGAN-FTL. In the framework, Federated Learning (FL) trains a global model on decentralized datasets of the clients with data privacy-preservation and Transfer Learning (TL) transfers the knowledge from the global model to a personalized model with a relatively small data volume. ACGAN acts as a data bridge to connect FL and TL by generating similar probability distribution data of clients since the client datasets in FL cannot be directly used in TL for data privacy-preservation. A real industrial scenario of pre-baked carbon anode quality prediction is applied to verify the performance of the proposed framework. The results show that ACGAN-FTL can not only obtain acceptable performance on 0.81 accuracy, 0.86 precision, 0.74 recall, and 0.79 F1 but also ensure data privacy-preservation in the whole learning process. Compared to the baseline method without FL and TL, the former metrics have increased by 13%, 11%, 16%, and 15% respectively. The experiments verify that the performance of the proposed ACGAN-FTL framework fulfills the requirements of industrial scenarios.

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“…A further class of ANNs is the convolutional neural networks (CNNs) that apply a convolution function to further refine the outputs. CNNs have been widely used in the material manufacturing sector, mainly using computer vision for detecting defects [ 32 , 85 , 86 , 87 ]. CNNs learn using images as input to the model and internal layers that can detect certain features, such as edges and lines in an object.…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

The Role of Machine Learning and Design of Experiments in the Advancement of Biomaterial and Tissue Engineering Research

et al. 2022

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Optimisation of tissue engineering (TE) processes requires models that can identify relationships between the parameters to be optimised and predict structural and performance outcomes from both physical and chemical processes. Currently, Design of Experiments (DoE) methods are commonly used for optimisation purposes in addition to playing an important role in statistical quality control and systematic randomisation for experiment planning. DoE is only used for the analysis and optimisation of quantitative data (i.e., number-based, countable or measurable), while it lacks the suitability for imaging and high dimensional data analysis. Machine learning (ML) offers considerable potential for data analysis, providing a greater flexibility in terms of data that can be used for optimisation and predictions. Its application within the fields of biomaterials and TE has recently been explored. This review presents the different types of DoE methodologies and the appropriate methods that have been used in TE applications. Next, ML algorithms that are widely used for optimisation and predictions are introduced and their advantages and disadvantages are presented. The use of different ML algorithms for TE applications is reviewed, with a particular focus on their use in optimising 3D bioprinting processes for tissue-engineered construct fabrication. Finally, the review discusses the future perspectives and presents the possibility of integrating DoE and ML in one system that would provide opportunities for researchers to achieve greater improvements in the TE field.

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Automated surface defect detection framework using machine vision and convolutional neural networks

Cited by 110 publications

References 47 publications

Magnetic Tile Surface Defect Detection Methodology Based on Self-Attention and Self-Supervised Learning

Magnetic Tile Surface Defect Detection Methodology Based on Self-Attention and Self-Supervised Learning

Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application

The Role of Machine Learning and Design of Experiments in the Advancement of Biomaterial and Tissue Engineering Research

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