AI-facilitated coating corrosion assessment system for productivity enhancement
Application of protective coatings is the primary method used to protect marine and offshore structures from coating breakdown and corrosion (CBC). Assessment of CBC is the major aspect in coating failure management. Subjective assessment methods cause unnecessary maintenance cost and higher risk of...
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| Veröffentlicht in: | IEEE Conference on Industrial Electronics and Applications (Online) S. 606 - 610 |
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| Format: | Tagungsbericht |
| Sprache: | Englisch |
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IEEE
01.05.2018
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| ISSN: | 2158-2297 |
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| Abstract | Application of protective coatings is the primary method used to protect marine and offshore structures from coating breakdown and corrosion (CBC). Assessment of CBC is the major aspect in coating failure management. Subjective assessment methods cause unnecessary maintenance cost and higher risk of failure. To improve efficiency and productivity, an integrated coating breakdown and corrosion (CBC) assessment system is developed. This AI-facilitated CBC inspection system implements a deep transfer learning technique to automate CBC assessment, it includes a faster region-base convolutional neural network (faster R-CNN) architecture and a vgg19 model for deep transfer learning, an instance-aware semantic segmentation method is developed for CBC measurement and grading. This method provides efficient inspection techniques for marine and offshore industries. |
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| AbstractList | Application of protective coatings is the primary method used to protect marine and offshore structures from coating breakdown and corrosion (CBC). Assessment of CBC is the major aspect in coating failure management. Subjective assessment methods cause unnecessary maintenance cost and higher risk of failure. To improve efficiency and productivity, an integrated coating breakdown and corrosion (CBC) assessment system is developed. This AI-facilitated CBC inspection system implements a deep transfer learning technique to automate CBC assessment, it includes a faster region-base convolutional neural network (faster R-CNN) architecture and a vgg19 model for deep transfer learning, an instance-aware semantic segmentation method is developed for CBC measurement and grading. This method provides efficient inspection techniques for marine and offshore industries. |
| Author | Tan, Estee Cai, Zhi Qiang Liu, Lili Zhen, Yongda Yin, Xi Jiang |
| Author_xml | – sequence: 1 givenname: Lili surname: Liu fullname: Liu, Lili organization: Department of Technology, Innovation and Enterprise, Singapore Polytechnic, Singapore, 139651 – sequence: 2 givenname: Estee surname: Tan fullname: Tan, Estee organization: Department of Technology, Innovation and Enterprise, Singapore Polytechnic, Singapore, 139651 – sequence: 3 givenname: Yongda surname: Zhen fullname: Zhen, Yongda organization: Department of Technology, Innovation and Enterprise, Singapore Polytechnic, Singapore, 139651 – sequence: 4 givenname: Xi Jiang surname: Yin fullname: Yin, Xi Jiang organization: Department of Technology, Innovation and Enterprise, Singapore Polytechnic, Singapore, 139651 – sequence: 5 givenname: Zhi Qiang surname: Cai fullname: Cai, Zhi Qiang organization: School of Electrical and Electronic Engineering, Singapore Polytechnic, Singapore, 139651 |
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| Snippet | Application of protective coatings is the primary method used to protect marine and offshore structures from coating breakdown and corrosion (CBC). Assessment... |
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| SubjectTerms | AI-facilitated CBC assessment system (A-CAS) coating breakdown and corrosion (CBC) inspection Coatings Corrosion deep transfer learning Electric breakdown Feature extraction Image edge detection Image segmentation Inspection instance-aware semantic segmentation |
| Title | AI-facilitated coating corrosion assessment system for productivity enhancement |
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