Weiwei Tan

Papers

Total Citations

H-Index

About

Weiwei Tan is a leading researcher in computer vision and intelligent power system inspection, with a focus on applying deep learning to critical infrastructure monitoring. His most influential work, "High-Voltage Transmission Line Foreign Object and Power Component Defect Detection Based on Improved YOLOv5" (2023), has garnered 19 citations and represents a significant advance in automated defect detection for electrical grids. Tan’s primary contributions lie in enhancing object detection algorithms—specifically through modifications to the YOLOv5 architecture—to identify foreign objects like kites or debris, as well as component defects such as insulator damage, on high-voltage transmission lines. This work directly addresses the need for safer, more efficient inspection methods, reducing reliance on manual patrols and improving grid reliability. By integrating attention mechanisms and multi-scale feature fusion, Tan’s approach achieves higher accuracy and real-time performance under complex outdoor conditions. His research bridges the gap between state-of-the-art AI and practical utility, offering scalable solutions for the energy sector. Tan’s achievements underscore his role in advancing smart grid technologies, making him a notable figure in applied computer vision for industrial safety and maintenance.