Qingying Yu

Anhui Institute of Information Technology, Anhui Normal University

Papers

Total Citations

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About

Qingying Yu is a researcher specializing in computer vision, deep learning, and mobile robotics, with a particular focus on enabling intelligent autonomous navigation in indoor environments. Yu's most influential contributions lie at the intersection of object recognition and robot perception, where their work has advanced the use of convolutional neural networks (CNNs) for identifying indoor objects — a critical capability for robots operating in complex, real-world spaces. Yu's 2017 paper on pre-trained CNNs for indoor object detection (15 citations) established a foundational pipeline leveraging transfer learning from public and private datasets, while the follow-up 2018 study (22 citations) integrated prior knowledge into deep learning frameworks to meaningfully improve detection precision — representing a notable step forward in the field. Together, these works reflect a consistent drive to make machine perception more robust and practically applicable. More recently, Yu has extended their focus to path planning algorithms for mobile robots, with work published in both 2022 and 2023 accumulating early citations, signaling growing interest in this direction. With a combined citation count surpassing 50, Yu's research offers valuable insights for students and engineers working on autonomous systems, human-robot interaction, and intelligent indoor navigation solutions.