Veysel Yuksel

About

Veysel Yuksel is a leading researcher in non-destructive testing (NDT) and intelligent pipeline inspection, with a focus on advancing safety and reliability in energy infrastructure. His work centers on developing deep learning models for the automated detection and quantification of defects in steel pipelines using magnetic flux leakage (MFL) signals. Yuksel’s most cited paper, “A Novel Cascaded Deep Learning Model for the Detection and Quantification of Defects in Pipelines via Magnetic Flux Leakage Signals” (2023, 55 citations), introduces a machine learning framework that interprets three-axis MFL data collected by semi-autonomous in-line inspection (ILI) robots. This contribution significantly improves the accuracy and speed of defect characterization, reducing reliance on manual signal analysis. His earlier work, “Defect Detection and Quantification from Magnetic Flux Leakage Signals Based on Deep Learning” (2022), further establishes his expertise in applying neural networks to industrial NDT challenges. Yuksel’s research bridges robotics, sensor technology, and artificial intelligence, offering practical solutions for pipeline integrity management. With growing citation impact, his innovations are poised to shape the future of autonomous infrastructure monitoring, making him a key figure in the intersection of deep learning and industrial safety.

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