Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition
The intelligent inspection of ceramic decorative defects is one of the hot research at present. This work aims to improve the defect inspection automation of finished decorative ceramic workpieces. First, it introduces the multi-target detection algorithm and compares the performance of different ne...
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| Veröffentlicht in: | Computational intelligence and neuroscience 2022-08, Vol.2022, p.1-9 |
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| description | The intelligent inspection of ceramic decorative defects is one of the hot research at present. This work aims to improve the defect inspection automation of finished decorative ceramic workpieces. First, it introduces the multi-target detection algorithm and compares the performance of different network models on the public data set. Second, the initial images are collected on the spot. The initial pictures are easy to produce noise in actual deployment, affecting the image quality. Therefore, image preprocessing is performed for the initial images, and a median filtering method is used to calculate the denoising. Finally, the original You Only Look Once version 3 network model is realized. Based on this, the decorative ceramic-oriented Automated Surface Defect Inspection model is proposed. Then, decorative ceramic defect images are inputted for model training. The experimental conclusions are deeply studied and analyzed. The results show that the proposed decorative ceramic-oriented Automated Surface Defect Inspection model based on Deep Learning technology has good feature extraction and inspection ability. The detection accuracy is 94.90% on the test set, and the detection speed reaches 25 frames per second. Compared with the traditional manual inspection method, the proposed model greatly improves the inspection effect and can meet the on-site inspection requirements of surface defects of decorative ceramics under complex backgrounds. It is of great significance to improve the quality inspection efficiency and economic benefits of China’s decorative ceramics industry. |
| doi_str_mv | 10.1155/2022/3983919 |
| format | Article |
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This work aims to improve the defect inspection automation of finished decorative ceramic workpieces. First, it introduces the multi-target detection algorithm and compares the performance of different network models on the public data set. Second, the initial images are collected on the spot. The initial pictures are easy to produce noise in actual deployment, affecting the image quality. Therefore, image preprocessing is performed for the initial images, and a median filtering method is used to calculate the denoising. Finally, the original You Only Look Once version 3 network model is realized. Based on this, the decorative ceramic-oriented Automated Surface Defect Inspection model is proposed. Then, decorative ceramic defect images are inputted for model training. The experimental conclusions are deeply studied and analyzed. The results show that the proposed decorative ceramic-oriented Automated Surface Defect Inspection model based on Deep Learning technology has good feature extraction and inspection ability. The detection accuracy is 94.90% on the test set, and the detection speed reaches 25 frames per second. Compared with the traditional manual inspection method, the proposed model greatly improves the inspection effect and can meet the on-site inspection requirements of surface defects of decorative ceramics under complex backgrounds. It is of great significance to improve the quality inspection efficiency and economic benefits of China’s decorative ceramics industry.</description><identifier>ISSN: 1687-5265</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2022/3983919</identifier><identifier>PMID: 36045964</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Accuracy ; Algorithms ; Artificial intelligence ; Artificial neural networks ; Automation ; Ceramic industry ; Ceramic materials ; Ceramics ; Ceramics industry ; Commercial printing industry ; Continuous casting ; Decoration ; Deep learning ; Defects ; Efficiency ; Feature extraction ; Frames per second ; Image filters ; Image quality ; Inspection ; Machine learning ; Neural networks ; Object recognition ; Printing industry ; Quality control equipment ; Research centers ; Surface defects ; Target detection ; Workpieces</subject><ispartof>Computational intelligence and neuroscience, 2022-08, Vol.2022, p.1-9</ispartof><rights>Copyright © 2022 Kaiyan Jin and Chunbin Wang.</rights><rights>COPYRIGHT 2022 John Wiley & Sons, Inc.</rights><rights>Copyright © 2022 Kaiyan Jin and Chunbin Wang. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2022 Kaiyan Jin and Chunbin Wang. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c453t-1991b36aa33cae5812db6e1398ba7e75fc918d5cabe85c9bf7ade8a05d82aa1c3</citedby><cites>FETCH-LOGICAL-c453t-1991b36aa33cae5812db6e1398ba7e75fc918d5cabe85c9bf7ade8a05d82aa1c3</cites><orcidid>0000-0001-8111-2407</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420580/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420580/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids></links><search><contributor>Kumar, Vijay</contributor><contributor>Vijay Kumar</contributor><creatorcontrib>Jin, Kaiyan</creatorcontrib><creatorcontrib>Wang, Chunbin</creatorcontrib><title>Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition</title><title>Computational intelligence and neuroscience</title><description>The intelligent inspection of ceramic decorative defects is one of the hot research at present. This work aims to improve the defect inspection automation of finished decorative ceramic workpieces. First, it introduces the multi-target detection algorithm and compares the performance of different network models on the public data set. Second, the initial images are collected on the spot. The initial pictures are easy to produce noise in actual deployment, affecting the image quality. Therefore, image preprocessing is performed for the initial images, and a median filtering method is used to calculate the denoising. Finally, the original You Only Look Once version 3 network model is realized. Based on this, the decorative ceramic-oriented Automated Surface Defect Inspection model is proposed. Then, decorative ceramic defect images are inputted for model training. The experimental conclusions are deeply studied and analyzed. The results show that the proposed decorative ceramic-oriented Automated Surface Defect Inspection model based on Deep Learning technology has good feature extraction and inspection ability. The detection accuracy is 94.90% on the test set, and the detection speed reaches 25 frames per second. Compared with the traditional manual inspection method, the proposed model greatly improves the inspection effect and can meet the on-site inspection requirements of surface defects of decorative ceramics under complex backgrounds. It is of great significance to improve the quality inspection efficiency and economic benefits of China’s decorative ceramics industry.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Automation</subject><subject>Ceramic industry</subject><subject>Ceramic materials</subject><subject>Ceramics</subject><subject>Ceramics industry</subject><subject>Commercial printing industry</subject><subject>Continuous casting</subject><subject>Decoration</subject><subject>Deep learning</subject><subject>Defects</subject><subject>Efficiency</subject><subject>Feature extraction</subject><subject>Frames per second</subject><subject>Image filters</subject><subject>Image quality</subject><subject>Inspection</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Object recognition</subject><subject>Printing industry</subject><subject>Quality control 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This work aims to improve the defect inspection automation of finished decorative ceramic workpieces. First, it introduces the multi-target detection algorithm and compares the performance of different network models on the public data set. Second, the initial images are collected on the spot. The initial pictures are easy to produce noise in actual deployment, affecting the image quality. Therefore, image preprocessing is performed for the initial images, and a median filtering method is used to calculate the denoising. Finally, the original You Only Look Once version 3 network model is realized. Based on this, the decorative ceramic-oriented Automated Surface Defect Inspection model is proposed. Then, decorative ceramic defect images are inputted for model training. The experimental conclusions are deeply studied and analyzed. The results show that the proposed decorative ceramic-oriented Automated Surface Defect Inspection model based on Deep Learning technology has good feature extraction and inspection ability. The detection accuracy is 94.90% on the test set, and the detection speed reaches 25 frames per second. Compared with the traditional manual inspection method, the proposed model greatly improves the inspection effect and can meet the on-site inspection requirements of surface defects of decorative ceramics under complex backgrounds. It is of great significance to improve the quality inspection efficiency and economic benefits of China’s decorative ceramics industry.</abstract><cop>New York</cop><pub>Hindawi</pub><pmid>36045964</pmid><doi>10.1155/2022/3983919</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-8111-2407</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Artificial intelligence Artificial neural networks Automation Ceramic industry Ceramic materials Ceramics Ceramics industry Commercial printing industry Continuous casting Decoration Deep learning Defects Efficiency Feature extraction Frames per second Image filters Image quality Inspection Machine learning Neural networks Object recognition Printing industry Quality control equipment Research centers Surface defects Target detection Workpieces |
| title | Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition |
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