Qipei Zhang
Papers
1
Total Citations
7
H-Index
1
About
Qipei Zhang’s research lies at the intersection of robotics, computer vision, and deep learning, with a particular focus on intelligent navigation and obstacle detection for industrial automation. His most cited work, “A Deep Learning and Depth Image based Obstacle Detection and Distance Measurement Method for Substation Patrol Robot” (2020), has garnered 7 citations, reflecting its practical significance in enhancing the safety and efficiency of substation maintenance. In this study, Zhang introduced a novel approach that combines convolutional neural networks with depth imaging to enable real-time obstacle recognition and precise distance estimation, addressing a critical challenge in autonomous patrol robotics. This contribution is especially valuable for improving the automation level of power infrastructure inspection, reducing human risk in hazardous environments. Zhang’s work exemplifies how deep learning can be applied to real-world engineering problems, bridging the gap between theoretical AI advances and industrial deployment. His research continues to influence the development of smarter, more reliable robotic systems for critical infrastructure monitoring.
Research Focus
Key Achievements
Top Papers
- 1