Lingli Yu

783 citations
65 papers · 543 · h-index 14

Impact in

Papers in

Lingli Yu

59 papers receiving 526 citations

Peers

Lingli Yu
Comparison fields: 5 of 80
  • Computer Vision and Pattern Recognition 282
  • Automotive Engineering 130
  • Control and Systems Engineering 158
  • Aerospace Engineering 96
  • Media Technology 33
Replace Zhe Xuanyuan with:
Zhe Xuanyuan China
Benjamin Pitzer United States
Sihai Tang United States
Linhai Xu China
Pablo Marín Spain
De Jong Yeong Ireland
Chen‐Chien Hsu Taiwan
Marius Zöllner Germany
Jingda Guo United States
Lingli Yu relative to Zhe Xuanyuan China Zhe Xuanyuan's profile →
Citations per field
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Zhe Xuanyuan · 1×
Citations per year

Countries citing papers authored by Lingli Yu

Since Specialization
Citations

This map shows the geographic impact of Lingli Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lingli Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lingli Yu more than expected).

Fields of papers citing papers by Lingli Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lingli Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lingli Yu. The network helps show where Lingli Yu may publish in the future.

Co-authors

The 25 scholars most cited alongside Lingli Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lingli Yu Line = papers co-authored together Lingli Yu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 65 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202256
2 201841
3 201836
4 201932
5 201729
6 202024
7 201622
8 202319
9 201716
10 201915
11 202414
12 201814
13 200913
14 201813
15 201212
16 201512
17 202311
18 20199
19 20229
20 20079

About Lingli Yu

Lingli Yu is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering, Artificial Intelligence, Automotive Engineering and Biomedical Engineering, having authored 65 papers that have together received 543 indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (17 papers), Autonomous Vehicle Technology and Safety (11 papers), Advanced Neural Network Applications (8 papers), Visual Attention and Saliency Detection (8 papers), Video Surveillance and Tracking Methods (6 papers), Robotics and Sensor-Based Localization (6 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Reinforcement Learning in Robotics (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (282 citations), Automotive Engineering (130 citations), Control and Systems Engineering (158 citations), Aerospace Engineering (96 citations) and Media Technology (33 citations). Lingli Yu has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Kaijun Zhou, Baifan Chen, Yuqian Zhao, Zixing Cai, Xiao‐Xin Yan, Fan Zhang, Biao Luo, Chunhua Yang, Xiaoyang Xiao and Yuqian Zhao. Their work appears in journals such as Sensors, Neurocomputing, Applied Intelligence, Future Internet and Applied Sciences.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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