Airbus Uses Artificial Intelligence and Deep Learning for Automatic Defect Detection
“Having the ability to test, modify, train, and test again the code in a short timeframe was key to success.”
Key Outcomes
- Used an integrated tool to design, train, and deploy deep learning models
- Performed interactive prototyping and testing in a very short amount of time
- Directly translated MATLAB code to CUDA code
How do you build a robust end-to-end AI model to automatically detect defects in pipes in an aircraft? That was the big challenge for Airbus, which used MATLAB® to quickly prototype and develop deep learning models to meet their needs.
Working with the MathWorks Consulting Services team, Airbus adopted MATLAB to address the three main steps in the process. The first step was to have an integrated tool to build and train deep learning models from scratch for approaches such as semantic segmentation, as well as an easy and interactive environment for labeling videos. The positions of the ventilation holes and the wires on the pipe, found by the deep learning model in MATLAB, were used to measure distances and angles required by industry standard. Next, they needed to be able to display the analysis of the defects in real time. The final step was to translate the MATLAB code to CUDA code automatically, without requiring any coding skills, to deploy it directly on the embedded system.
