Bingjie Chen
Papers
1
Total Citations
10
H-Index
1
About
Bingjie Chen is a leading researcher in pipeline integrity and intelligent defect detection, with a primary focus on the safety and maintenance of natural gas infrastructure. Her most cited work, "Automatic detection of internal corrosion defect in a natural gas gathering pipeline using improved YOLOv5 model" (2023, 10 citations), tackles a critical challenge in the energy sector: the automated, accurate identification of internal corrosion in lengthy pipelines. By enhancing the YOLOv5 deep learning model, Chen’s research enables rapid, reliable detection of defects from endoscopic inspection images, directly addressing a major operational hurdle for gas companies—preventing costly failures and ensuring pipeline longevity. This contribution bridges computer vision and industrial safety, offering a practical, AI-driven solution for real-world asset management. Chen’s work is notable for its applied impact, providing a scalable method that reduces human error and inspection time. Her research continues to advance the field of nondestructive evaluation, making her a key figure in the intersection of machine learning and energy infrastructure resilience.
Research Focus
Key Achievements
Top Papers
- 1