X-ray Anomaly Detection in Industrial Pipelines
As Deep Neural Network (DNN)-based algorithms are improving, pivotal changes are happening towards efficient and effective automation in the field of industrial inspection. In the scope of our project, we analyze X-ray images of steel pipelines to detect the presence of corrosion in a novel way. In our industrial scenario, a drone lands a crawler that is equipped with an X-ray system on top of insulated pipelines to perform X-ray scans which are able to penetrate only the insulation, due to power consumption limitations.
In this paper, we use modern unsupervised anomaly detection algorithms to detect the presence of corrosion, and the results are quite promising. Moreover, to compare several state-of-the-art approaches in terms of robustness to noise, we simulate two types of noise that can occur: (i) Poisson Noise, (ii) Motion Blur Noise. We conclude that the problem we are dealing with can be handled sufficiently well with state-of-the-art approaches, and that in the scenario of noise, the most robust algorithms are based on memory banks and teacher-student architectures.