In the scheme of Non Destructive Testing (NDT), defect detection is an important process. Traditional image processing techniques have successfully been used for
defect recognition. Usage of machine learning techniques is still in the initial stages of development. Convolution Neural Networks (CNN) is widely used for object
classification one such scenario is defect classification in weld tubes. With the advent of deep learning techniques such as transfer learning, we can transfer knowledge
gained in one domain successfully into other. Pre-trained models successfully learn features from large scale datasets that can be used for in domains having sparse
data and smaller datasets.
The aim of this work is to help a manual inspector in recognition of defects on the weld tubes. With a given set of images, we proceed by forming unique pipeline
architecture for automatic defect recognition. The research in this thesis focuses on extraction of welds using image segmentation techniques, creating a dataset of defects
and using it to on pre-trained Convolution Neural Networks of VGG16, VGG19, Inception V3 and ResNet101. We evaluate the models on different metrics finding
the best suited model for the created dataset. Further a prototype sliding window solution is used to find defects over the extracted weld region. We also present the
limitations of this approach and suggest modifications that can be implemented in the future.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:73092 |
| Date | 09 December 2020 |
| Creators | Sundar Rajan, Sarvesh |
| Contributors | Hardt, Wolfram, Schuster, Alfons, Mayer, Monika, Saleh, Shadi, Technische Universität Chemnitz |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:masterThesis, info:eu-repo/semantics/masterThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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