Jie Geng

Papers

Total Citations

H-Index

About

Jie Geng’s research centers on intelligent non-destructive testing (NDT) and robotic inspection systems, with a particular focus on pressure vessel safety and defect image recognition. His most cited work, “Application of Neural Network Technology in Defect Image Recognition” (2021), introduces a wall-climbing robot equipped with visual sensors that performs real-time magnetic particle testing—a breakthrough that simultaneously enhances detection accuracy and operational speed. By integrating neural network algorithms into the inspection process, Geng’s approach automates the identification of surface and near-surface defects, reducing human error and improving reliability in critical industrial settings. Although his citation count is currently modest (2 citations for his top paper), the practical implications of his work are significant: it offers a scalable, cost-effective solution for regular pressure vessel maintenance, directly addressing safety risks in energy, chemical, and manufacturing sectors. Geng’s contributions bridge robotics, computer vision, and deep learning, positioning him as an emerging voice in applied NDT research. His work is especially relevant for students and engineers seeking to modernize traditional inspection methods through autonomous systems and AI-driven analysis.