MFL Data Preprocessing and CNN-based Oil Pipeline Defects Detection
Iurii Katser Vyacheslav Kozitsin Igor Mozolin
Recently, the application of computer vision for anomaly detection has been
under attention in several industrial fields. An important example is oil
pipeline defect detection. Failure of one oil pipeline can interrupt the
operation of the entire transportation system or cause a far-reaching failure.
The automated defect detection could significantly decrease the inspection time
and the related costs. However, there is a gap in the related literature when
it comes to dealing with this task. The existing studies do not sufficiently
cover the research of the Magnetic Flux Leakage data and the preprocessing
techniques that allow overcoming the limitations set by the available data.
This work focuses on alleviating these issues. Moreover, in doing so, we
exploited the recent convolutional neural network structures and proposed
robust approaches, aiming to acquire high performance considering the related
metrics. The proposed approaches and their applicability were verified using
real-world data.