Identifying Capsule Defect Based on an Improved Convolutional Neural Network
Capsules are commonly used as containers for most pharmaceuticals, and capsule quality is closely related to human health. Given the actual demand for capsule production, this study proposes a capsule defect detection and recognition method based on an improved convolutional neural network (CNN) alg...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/8887723 |
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| _version_ | 1849397954919530496 |
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| author | Junlin Zhou Jiao He Guoli Li Yongbin Liu |
| author_facet | Junlin Zhou Jiao He Guoli Li Yongbin Liu |
| author_sort | Junlin Zhou |
| collection | DOAJ |
| description | Capsules are commonly used as containers for most pharmaceuticals, and capsule quality is closely related to human health. Given the actual demand for capsule production, this study proposes a capsule defect detection and recognition method based on an improved convolutional neural network (CNN) algorithm. The algorithm is used for defect detection and classification in capsule production. Defective and qualified capsule images in the actual production are collected as samples. Then, a deep learning model based on the improved CNN is designed to train and test a capsule image dataset and identify defective capsules. The improved CNN algorithm is based on regularization and the Adam optimizer (RACNN), on which a dropout layer and L2_regularization are added between the full connection and the output layer to solve the overfitting problem. The Adam optimizer is introduced to accelerate model training and improve model convergence. Then, cross entropy is used as a loss function to measure the prediction performance of the model. By comparing the results of RACNN with different parameters, a detection method based on the optimal parameters of the RACNN model is finally selected. Results show a 97.56% recognition accuracy of the proposed method. Hence, this method could be used for the automatic identification and classification of defective capsules. |
| format | Article |
| id | doaj-art-a5de96ed35e84dfb9b5db540309b6048 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-a5de96ed35e84dfb9b5db540309b60482025-08-20T03:38:48ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/88877238887723Identifying Capsule Defect Based on an Improved Convolutional Neural NetworkJunlin Zhou0Jiao He1Guoli Li2Yongbin Liu3College of Electrical Engineering and Automation, Anhui University, Hefei 230601, ChinaCollege of Electrical Engineering and Automation, Anhui University, Hefei 230601, ChinaCollege of Electrical Engineering and Automation, Anhui University, Hefei 230601, ChinaCollege of Electrical Engineering and Automation, Anhui University, Hefei 230601, ChinaCapsules are commonly used as containers for most pharmaceuticals, and capsule quality is closely related to human health. Given the actual demand for capsule production, this study proposes a capsule defect detection and recognition method based on an improved convolutional neural network (CNN) algorithm. The algorithm is used for defect detection and classification in capsule production. Defective and qualified capsule images in the actual production are collected as samples. Then, a deep learning model based on the improved CNN is designed to train and test a capsule image dataset and identify defective capsules. The improved CNN algorithm is based on regularization and the Adam optimizer (RACNN), on which a dropout layer and L2_regularization are added between the full connection and the output layer to solve the overfitting problem. The Adam optimizer is introduced to accelerate model training and improve model convergence. Then, cross entropy is used as a loss function to measure the prediction performance of the model. By comparing the results of RACNN with different parameters, a detection method based on the optimal parameters of the RACNN model is finally selected. Results show a 97.56% recognition accuracy of the proposed method. Hence, this method could be used for the automatic identification and classification of defective capsules.http://dx.doi.org/10.1155/2020/8887723 |
| spellingShingle | Junlin Zhou Jiao He Guoli Li Yongbin Liu Identifying Capsule Defect Based on an Improved Convolutional Neural Network Shock and Vibration |
| title | Identifying Capsule Defect Based on an Improved Convolutional Neural Network |
| title_full | Identifying Capsule Defect Based on an Improved Convolutional Neural Network |
| title_fullStr | Identifying Capsule Defect Based on an Improved Convolutional Neural Network |
| title_full_unstemmed | Identifying Capsule Defect Based on an Improved Convolutional Neural Network |
| title_short | Identifying Capsule Defect Based on an Improved Convolutional Neural Network |
| title_sort | identifying capsule defect based on an improved convolutional neural network |
| url | http://dx.doi.org/10.1155/2020/8887723 |
| work_keys_str_mv | AT junlinzhou identifyingcapsuledefectbasedonanimprovedconvolutionalneuralnetwork AT jiaohe identifyingcapsuledefectbasedonanimprovedconvolutionalneuralnetwork AT guolili identifyingcapsuledefectbasedonanimprovedconvolutionalneuralnetwork AT yongbinliu identifyingcapsuledefectbasedonanimprovedconvolutionalneuralnetwork |