The Experts below are selected from a list of 16431 Experts worldwide ranked by ideXlab platform
Zhouping Yin - One of the best experts on this subject based on the ideXlab platform.
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multiscale feature clustering based fully convolutional autoencoder for fast accurate visual inspection of texture surface Defects
IEEE Transactions on Automation Science and Engineering, 2019Co-Authors: Hua Yang, Kaiyou Song, Yifan Chen, Zhouping YinAbstract:Visual inspection of texture surface Defects is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. Current visual inspection methods cannot simultaneously and efficiently inspect various types of texture Defects due to either the low discriminative capabilities of handcrafted features or their time-consuming sliding-window strategy. In this paper, we present a novel unsupervised multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) method that efficiently and accurately inspects various types of texture Defects based on a small number of Defect-free texture samples. The proposed MS-FCAE method utilizes multiple FCAE subnetworks at different scale levels to reconstruct several textured background Images. The residual Images are obtained by subtracting these texture backgrounds from the input Image individually; then, they are fused into one Defect Image. To maximize the efficiency, each FCAE subnetwork utilizes fully convolutional neural networks to extract the original feature maps directly from the input Images. Meanwhile, each FCAE subnetwork performs feature clustering to improve the discriminant power of the encoded feature maps. The proposed MS-FCAE method is evaluated on several texture surface inspection data sets both qualitatively and quantitatively. This method achieves a Precision of 92.0% while requiring only 82 ms for input Images of $1920\times 1080$ pixels. The extensive experimental results demonstrate that MS-FCAE achieves highly efficient and state-of-the-art inspection accuracy. Note to Practitioners —Most conventional visual inspection methods can address only one specific type of texture Defect, while multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) can simultaneously and accurately inspect various types of texture surface Defects, such as those of thin-film transistor liquid crystal displays, wood, fabrics, and ceramic tiles. Furthermore, MS-FCAE requires only a small number of surface texture samples to learn a robust network model, and its training requires no Defect samples. This is extremely important for industrial applications because identifying and labeling Defect samples is difficult. Moreover, MS-FCAE can be applied to online visual inspection utilizing a graphics processing unit-based parallel processing strategy.
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transfer learning based online mura Defect classification
IEEE Transactions on Semiconductor Manufacturing, 2018Co-Authors: Hua Yang, Shuang Mei, Kaiyou Song, Bo Tao, Zhouping YinAbstract:Flat panel displays, such as the thin film transistor liquid crystal display, the organic light-emitting diode, and the polymer light-emitting diode, have been widely applied in many fields in recent decades. To ensure the quality of these displays, Defect inspection is crucial. Mura Defects, which are phenomena of uneven screen displays, are the most challenging visual Defects to detect. This paper presents an online sequential classifier and transfer learning (OSC-TL) method for the online training and classification of Mura Defects. OSC-TL is a new method that combines a deep convolutional feature extractor and a sequential extreme learning machine classifier. It makes online sequential training in a production line possible. To demonstrate the performance of the OSC-TL method, several experiments are performed to compare the results of this method with those of other popular classification algorithms. The experimental results show that the computational resources and time consumed by OSC-TL are well below those of other common methods because of the feature transfer and the online sequential classification strategies. Consequently, the OSC-TL method has been implemented in our automated optical inspection equipment to perform online Mura Defect classification. It is able to learn and recognize a Mura Defect Image within 1.5 milliseconds.
Hua Yang - One of the best experts on this subject based on the ideXlab platform.
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multiscale feature clustering based fully convolutional autoencoder for fast accurate visual inspection of texture surface Defects
IEEE Transactions on Automation Science and Engineering, 2019Co-Authors: Hua Yang, Kaiyou Song, Yifan Chen, Zhouping YinAbstract:Visual inspection of texture surface Defects is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. Current visual inspection methods cannot simultaneously and efficiently inspect various types of texture Defects due to either the low discriminative capabilities of handcrafted features or their time-consuming sliding-window strategy. In this paper, we present a novel unsupervised multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) method that efficiently and accurately inspects various types of texture Defects based on a small number of Defect-free texture samples. The proposed MS-FCAE method utilizes multiple FCAE subnetworks at different scale levels to reconstruct several textured background Images. The residual Images are obtained by subtracting these texture backgrounds from the input Image individually; then, they are fused into one Defect Image. To maximize the efficiency, each FCAE subnetwork utilizes fully convolutional neural networks to extract the original feature maps directly from the input Images. Meanwhile, each FCAE subnetwork performs feature clustering to improve the discriminant power of the encoded feature maps. The proposed MS-FCAE method is evaluated on several texture surface inspection data sets both qualitatively and quantitatively. This method achieves a Precision of 92.0% while requiring only 82 ms for input Images of $1920\times 1080$ pixels. The extensive experimental results demonstrate that MS-FCAE achieves highly efficient and state-of-the-art inspection accuracy. Note to Practitioners —Most conventional visual inspection methods can address only one specific type of texture Defect, while multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) can simultaneously and accurately inspect various types of texture surface Defects, such as those of thin-film transistor liquid crystal displays, wood, fabrics, and ceramic tiles. Furthermore, MS-FCAE requires only a small number of surface texture samples to learn a robust network model, and its training requires no Defect samples. This is extremely important for industrial applications because identifying and labeling Defect samples is difficult. Moreover, MS-FCAE can be applied to online visual inspection utilizing a graphics processing unit-based parallel processing strategy.
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transfer learning based online mura Defect classification
IEEE Transactions on Semiconductor Manufacturing, 2018Co-Authors: Hua Yang, Shuang Mei, Kaiyou Song, Bo Tao, Zhouping YinAbstract:Flat panel displays, such as the thin film transistor liquid crystal display, the organic light-emitting diode, and the polymer light-emitting diode, have been widely applied in many fields in recent decades. To ensure the quality of these displays, Defect inspection is crucial. Mura Defects, which are phenomena of uneven screen displays, are the most challenging visual Defects to detect. This paper presents an online sequential classifier and transfer learning (OSC-TL) method for the online training and classification of Mura Defects. OSC-TL is a new method that combines a deep convolutional feature extractor and a sequential extreme learning machine classifier. It makes online sequential training in a production line possible. To demonstrate the performance of the OSC-TL method, several experiments are performed to compare the results of this method with those of other popular classification algorithms. The experimental results show that the computational resources and time consumed by OSC-TL are well below those of other common methods because of the feature transfer and the online sequential classification strategies. Consequently, the OSC-TL method has been implemented in our automated optical inspection equipment to perform online Mura Defect classification. It is able to learn and recognize a Mura Defect Image within 1.5 milliseconds.
Kaiyou Song - One of the best experts on this subject based on the ideXlab platform.
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multiscale feature clustering based fully convolutional autoencoder for fast accurate visual inspection of texture surface Defects
IEEE Transactions on Automation Science and Engineering, 2019Co-Authors: Hua Yang, Kaiyou Song, Yifan Chen, Zhouping YinAbstract:Visual inspection of texture surface Defects is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. Current visual inspection methods cannot simultaneously and efficiently inspect various types of texture Defects due to either the low discriminative capabilities of handcrafted features or their time-consuming sliding-window strategy. In this paper, we present a novel unsupervised multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) method that efficiently and accurately inspects various types of texture Defects based on a small number of Defect-free texture samples. The proposed MS-FCAE method utilizes multiple FCAE subnetworks at different scale levels to reconstruct several textured background Images. The residual Images are obtained by subtracting these texture backgrounds from the input Image individually; then, they are fused into one Defect Image. To maximize the efficiency, each FCAE subnetwork utilizes fully convolutional neural networks to extract the original feature maps directly from the input Images. Meanwhile, each FCAE subnetwork performs feature clustering to improve the discriminant power of the encoded feature maps. The proposed MS-FCAE method is evaluated on several texture surface inspection data sets both qualitatively and quantitatively. This method achieves a Precision of 92.0% while requiring only 82 ms for input Images of $1920\times 1080$ pixels. The extensive experimental results demonstrate that MS-FCAE achieves highly efficient and state-of-the-art inspection accuracy. Note to Practitioners —Most conventional visual inspection methods can address only one specific type of texture Defect, while multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) can simultaneously and accurately inspect various types of texture surface Defects, such as those of thin-film transistor liquid crystal displays, wood, fabrics, and ceramic tiles. Furthermore, MS-FCAE requires only a small number of surface texture samples to learn a robust network model, and its training requires no Defect samples. This is extremely important for industrial applications because identifying and labeling Defect samples is difficult. Moreover, MS-FCAE can be applied to online visual inspection utilizing a graphics processing unit-based parallel processing strategy.
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transfer learning based online mura Defect classification
IEEE Transactions on Semiconductor Manufacturing, 2018Co-Authors: Hua Yang, Shuang Mei, Kaiyou Song, Bo Tao, Zhouping YinAbstract:Flat panel displays, such as the thin film transistor liquid crystal display, the organic light-emitting diode, and the polymer light-emitting diode, have been widely applied in many fields in recent decades. To ensure the quality of these displays, Defect inspection is crucial. Mura Defects, which are phenomena of uneven screen displays, are the most challenging visual Defects to detect. This paper presents an online sequential classifier and transfer learning (OSC-TL) method for the online training and classification of Mura Defects. OSC-TL is a new method that combines a deep convolutional feature extractor and a sequential extreme learning machine classifier. It makes online sequential training in a production line possible. To demonstrate the performance of the OSC-TL method, several experiments are performed to compare the results of this method with those of other popular classification algorithms. The experimental results show that the computational resources and time consumed by OSC-TL are well below those of other common methods because of the feature transfer and the online sequential classification strategies. Consequently, the OSC-TL method has been implemented in our automated optical inspection equipment to perform online Mura Defect classification. It is able to learn and recognize a Mura Defect Image within 1.5 milliseconds.
Mingming Jiang - One of the best experts on this subject based on the ideXlab platform.
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an end to end steel strip surface Defects recognition system based on convolutional neural networks
Steel Research International, 2017Co-Authors: Mingming JiangAbstract:Steel strip surface Defects recognition is very important to steel strip production and quality control, which needs further improvement. In this paper, an end-to-end surface Defects recognition system is proposed for steel strip surface inspection. This system is based on the symmetric surround saliency map for surface Defects detection and deep convolutional neural networks (CNNs) which directly use the Defect Image as input and Defect category as output for seven classes of steel strip Defects classification. The CNNs are trained purely on raw Defect Images and learned Defect features from the training of network, which avoiding the separation between feature extraction and Image classification, so that forms an end-to-end Defects recognition pipeline. To further illustrate the superiority of the Defect recognition methods with CNNs, an authoritative and standard steel strip surface Defect dataset − NEU is also used to evaluate the Defect recognition effect using CNNs. Experimental results demonstrate that the proposed methods perform well in steel strip surface Defect detection of different types and achieve a high recognition rate for Defect Images. In addition, a series of data augmentation methods are discussed to analyze its effect on avoiding over-fitting for Defects recognition.
Huang Kunshan - One of the best experts on this subject based on the ideXlab platform.
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wood Defect detection method based on deep learning and system thereof
2017Co-Authors: Huang KunshanAbstract:The invention provides a wood Defect detection method based on deep learning. The method comprises the following steps of collecting Images; segmenting the Images into Image blocks with a same size; selecting Defect Image blocks with different types and non-Defective Image blocks as a training sample set; using the training sample set to train a deep learning algorithm in an off-line mode; and using a trained deep learning algorithm to detect and identify Defects of a wood Image in an online mode and the like. In the invention, through the powerful deep learning algorithm, Defects on different complex texture wood surfaces are detected and identified in high precision and online modes and a problem that a traditional Image processing algorithm can not solve is solved. The invention also provides a wood Defect detection system based on deep learning. Through cooperation among an Image acquisition module, a deep learning algorithm processing module and a control execution module, a detection speed can be effectively accelerated and practicality is increased.
