Xiangkai Shen
Papers
1
Total Citations
65
H-Index
1
About
Dr. Xiangkai Shen is a leading researcher in intelligent nondestructive testing and pipeline defect detection, with a focus on advancing magnetic flux leakage (MFL) technology. His most cited work, "An Intelligent Defect Detection Approach Based on Cascade Attention Network Under Complex Magnetic Flux Leakage Signals" (2022, 65 citations), addresses a critical challenge in pipeline inspection: the poor accuracy of defect detection caused by noisy, complex MFL signals from real-world environments. By introducing a novel cascade attention network, Shen’s approach significantly improves the precision and reliability of automated defect identification, enabling more robust performance for MFL detection robots operating under challenging field conditions. This contribution has direct implications for the safety and maintenance of oil and gas pipelines, reducing the risk of undetected failures. With growing citation impact, Shen’s work is recognized as a key step toward integrating deep learning with industrial inspection systems. His research continues to bridge the gap between advanced computer vision techniques and practical pipeline monitoring, making him a notable figure in the field of intelligent defect diagnostics.
Research Focus
Key Achievements
Top Papers
- 1