Mingchi Feng

Chongqing University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Mingchi Feng is a researcher at the forefront of computer vision and intelligent robotics, with a primary focus on object detection in natural environments and visual odometry for autonomous systems. His most impactful work, "Research on tomato detection in natural environment based on RC-YOLOv4" (2022), has garnered 73 citations, demonstrating its significance in agricultural automation and deep learning-based detection. This contribution addresses the critical challenge of accurately identifying crops in complex, unstructured settings, advancing precision agriculture. More recently, Feng has explored visual odometry (VO) using deep learning for joint semantic segmentation (2024), tackling persistent issues like scale ambiguity and pose estimation errors that hinder autonomous vehicles, robots, and augmented reality applications. By integrating semantic understanding into motion estimation, his work aims to enhance the reliability and accuracy of visual navigation systems. Feng’s research bridges the gap between theoretical deep learning models and practical, real-world deployment, making him a notable contributor to both agricultural technology and autonomous navigation. His growing citation record reflects the relevance and potential impact of his innovations for students and researchers in computer vision and robotics.