Hejie Wang

Papers

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About

Hejie Wang is a researcher at the forefront of intelligent robotics and computer vision, with a focused application in critical energy infrastructure. His work addresses the pressing need for automation in the maintenance of Ultra-High Voltage (UHV) substations, where manual intervention poses significant risks. Wang’s key contribution is the development of a visual system for an oil sampling robot, integrating the YOLO v5 object detection algorithm with the OpenCV model. This system enables precise, autonomous identification and manipulation of transformer oil ports, directly tackling the problem of oil aging that can lead to power system breakdowns. While his most cited paper, "Visual system for oil sampling robot based on YOLO v5 and OpenCV model" (2022), has garnered 2 citations, its impact lies in its practical, safety-critical application. By pioneering a robotic solution for routine oil testing in high-voltage environments, Wang’s work demonstrates a tangible path toward reducing human risk and improving the reliability of long-term power grid operations, marking him as an emerging contributor to the fields of industrial automation and intelligent maintenance systems.