Qiaosheng Feng
Papers
1
Total Citations
12
H-Index
1
About
Qiaosheng Feng is a researcher whose work sits at the intersection of computer vision and smart grid automation, with a particular focus on enhancing the intelligence of substation inspection systems. His primary research areas include multi-scale feature fusion for object detection and the application of deep learning to industrial instrumentation recognition. Feng’s most notable contribution, detailed in his highly cited 2022 paper “Substation instrumentation target detection based on multi‐scale feature fusion,” addresses a critical bottleneck in smart grid maintenance: the low efficiency and accuracy of automated meter reading by inspection robots. By proposing a novel algorithm that fuses features across different scales, he significantly improves the detection of diverse substation instruments, enabling robots to operate with higher precision and speed. This work, which has already garnered 12 citations, directly supports the broader push toward fully automated, high-reliability power infrastructure. Feng’s research bridges the gap between advanced computer vision techniques and real-world industrial deployment, making him a key contributor to the evolution of intelligent power systems.
Research Focus
Key Achievements
Top Papers
- 1