Kai Ye

PLA Information Engineering University, Guangdong Open University

Papers

Total Citations

H-Index

About

Kai Ye is a researcher whose work bridges the fields of astronomical instrumentation and applied computer vision. His primary research areas include high-precision celestial geodetic surveying, automated astronomical measurement systems, and intelligent image recognition. Ye’s major contribution lies in advancing the automation and accuracy of astronomical surveys. His most cited work, "Automatic Astronomical Survey Method Based on Video Measurement Robot" (2020, 9 citations), introduces a system using CCD imaging to eliminate the subjective errors of traditional manual observation—the so-called personal and instrumental equation—thereby significantly improving measurement reliability. This foundational work is complemented by his studies on star map processing, such as the "One-dimensional Maximum Entropy Image Segmentation Algorithm" (2018, 4 citations), which tackles the difficult problem of extracting star targets from complex sky backgrounds. Demonstrating versatility, Ye has also applied deep learning to environmental challenges, as seen in his 2023 paper on "Image recognition of garbage classification based on YOLOv8" (4 citations). By integrating robotics, image segmentation, and neural networks, Kai Ye’s research contributes to both the precision of astronomical measurement and the practical deployment of intelligent vision systems.