Yanfei Zhou
Papers
1
Total Citations
2
H-Index
1
About
Yanfei Zhou is a researcher whose work centers on the intersection of computer vision and industrial automation, with a particular focus on object detection algorithms for real-world applications. Zhou’s most cited study, “Detection and Identification of Digital Display Meter of Distribution Cabinet Based on YOLOv5 Algorithm” (2022), demonstrates a practical contribution to smart infrastructure by applying the YOLOv5 deep learning framework to automate the reading of digital meters in electrical distribution cabinets. This work addresses a critical need for efficient, contactless monitoring in power systems, reducing human error and improving operational safety. While still early in their career, Zhou’s research has already garnered attention, with the paper accumulating citations that signal growing interest from both the computer vision and energy sectors. By bridging state-of-the-art AI techniques with industrial maintenance challenges, Zhou is helping to pave the way for more intelligent, automated inspection systems. Their work is particularly relevant for researchers exploring edge deployment of lightweight detection models in resource-constrained environments, and for engineers seeking robust solutions for legacy equipment digitization.
Research Focus
Key Achievements
Top Papers
- 1