Mahmut Omer Basturk

About

Mahmut Omer Basturk is a leading researcher in non-destructive testing (NDT) and intelligent infrastructure monitoring, with a specialized focus on the automated inspection of steel pipelines. His work centers on developing advanced machine learning and deep learning models to interpret magnetic flux leakage (MFL) signals, which are critical for detecting and quantifying defects in pipelines without causing damage. Basturk’s most notable contribution is his 2023 paper, "A Novel Cascaded Deep Learning Model for the Detection and Quantification of Defects in Pipelines via Magnetic Flux Leakage Signals," which has garnered 55 citations. This work introduces a semi-autonomous in-line inspection (ILI) robot equipped with MFL sensors, paired with a cascaded deep learning framework that significantly improves the accuracy of defect detection and sizing. By bridging the gap between raw sensor data and actionable maintenance insights, Basturk’s research enhances the safety and reliability of energy transportation infrastructure. His earlier 2022 paper on defect quantification further underscores his commitment to advancing NDT methodologies. Basturk’s innovative integration of robotics and AI positions him as a key contributor to the future of automated pipeline integrity management.

Research Focus

Key Achievements

Top Papers

1. 1
   A Novel Cascaded Deep Learning Model for the Detection and Quantification of Defects in Pipelines via Magnetic Flux Leakage Signals

   55 citations · 2023
2. 2
   Defect Detection and Quantification from Magnetic Flux Leakage Signals Based on Deep Learning

   3 citations · 2022

Key Collaborators

Contact & Links