Ren Jin

Beijing Institute of Technology, Hefei University of Technology

Papers

Total Citations

H-Index

About

Ren Jin is a researcher focused on autonomous systems and computer vision, with particular expertise in unmanned aerial vehicle (UAV) navigation and depth perception. His most impactful work addresses the critical challenge of autonomous landing for UAVs in complex environments. In his landmark 2018 paper, "Ellipse proposal and convolutional neural network discriminant for autonomous landing marker detection" (33 citations), Jin developed a novel approach combining ellipse proposal algorithms with CNN-based discriminants to efficiently detect landing markers under real-world conditions, overcoming the computational constraints of airborne systems. This work has become a reference point for researchers tackling practical UAV autonomy. Jin has also contributed to monocular depth estimation, proposing a structured forest framework in his 2016 paper (8 citations) to infer depth information from single RGB images—a technique vital for robot navigation and motion capture. His research bridges the gap between theoretical computer vision and deployable autonomous systems, addressing the computational limitations that often hinder real-world applications. Jin's work continues to influence the development of more capable, self-navigating aerial robots.