| KEYWORD |
Artificial Intelligence-based non-destructive testing system for the identification of oil & gas weld pipe defects
Thesis in external company
keywords ANOMALY DETECTION, ARTIFICIAL INTELLIGENCE, SEMANTIC SEGMENTATION
Reference persons TATIANA TOMMASI
External reference persons Francesco Cannarile, ENI
Roberta Bianchi, ENI
Research Groups DAUIN - GR-23 - VANDAL - Visual and Multimodal Applied Learning Lab
Thesis type RESEARCH THESIS WITH A COMPANY
Description Non-Destructive Testing (NDT) is a testing and analysis technique used by industry to evaluate the properties of a material, component, structure or system for characteristic differences or welding defects and discontinuities without causing damage to the original part.
Contractors involved in the construction of oil & gas facilities (both upstream and downstream) make available NDT operators who are responsible for visually identifying any indication, i.e., the response or evidence of any anomaly/defects from a non-destructive examination on piping welds from the reading of x-ray plates/films NDT images. In this light, this thesis aims at developing a computer vision model for piping weld indications identification relying on artificial intelligence which automatically detects and diagnoses possible indications from x-ray plates/films NDT images.
This intelligent system should be able to perform the following tasks:
1. Quality image check: checking of required Image Quality Indicators (IQIs)
2. Detection: detection of the presence of any indication (anomaly/defect);
3. Characterization: diagnosing the type of the indication (if any);
4. Size assessment: assessing the size of the indication (if any);
5. Compliance check: verifying if the size of the indication is greater than some fixed
default thresholds.
See also thesis_proposal+paper_letteratura.pdf
Required skills The successful candidate is expected to have Good knowledge of Python programming and main libraries for data science (numpy, pandas, scipy, etc), computer vision (OpenCV, scikit-image, etc.) and at least one deep learning framework (PyTorch is preferred to Tensorflow).
Deadline 23/05/2024
PROPONI LA TUA CANDIDATURA