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
Hauptverfasser: Liu, Lili, Tan, Estee, Zhen, Yongda, Yin, Xi Jiang, Cai, Zhi Qiang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2018
Schlagworte:
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
ISSN:2158-2297
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Zusammenfassung: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.
ISSN:2158-2297
DOI:10.1109/ICIEA.2018.8397787