Linhui Dai

1.1k total citations · 2 hit papers
8 papers, 559 citations indexed

About

Linhui Dai is a scholar working on Computer Vision and Pattern Recognition, Ocean Engineering and Aerospace Engineering. According to data from OpenAlex, Linhui Dai has authored 8 papers receiving a total of 559 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 2 papers in Ocean Engineering and 2 papers in Aerospace Engineering. Recurrent topics in Linhui Dai's work include Advanced Neural Network Applications (3 papers), Advanced Vision and Imaging (2 papers) and Advanced Image Processing Techniques (2 papers). Linhui Dai is often cited by papers focused on Advanced Neural Network Applications (3 papers), Advanced Vision and Imaging (2 papers) and Advanced Image Processing Techniques (2 papers). Linhui Dai collaborates with scholars based in China and Canada. Linhui Dai's co-authors include Runwei Ding, Hong Liu, Guangpeng Li, Pinhao Song, Tao Wang, Zhan Chen, Jun Chen, Xiaohong Liu, Shengquan Li and Xiaochuan Zhang and has published in prestigious journals such as Neurocomputing, IEEE Transactions on Multimedia and IEEE Transactions on Broadcasting.

In The Last Decade

Linhui Dai

8 papers receiving 538 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

TDD‐net: a tiny defect detection network for printed circuit boards

2019 277 citations Runwei Ding, Linhui Dai et al. CAAI Transactions on Intelligence Technology profile →
Boosting R-CNN: Reweighting R-CNN samples by RPN’s error for underwater object detection

2023 155 citations Pinhao Song, Linhui Dai et al. Neurocomputing profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Linhui Dai China	6	383	272	70	67	49	8	559
Xuewu Zhang China	8	222 0.6×	155 0.6×	55 0.8×	80 1.2×	21 0.4×	34	424
Yiquan Wu China	14	504 1.3×	68 0.3×	41 0.6×	167 2.5×	46 0.9×	51	697
Hanyu Li China	7	688 1.8×	32 0.1×	41 0.6×	195 2.9×	35 0.7×	14	831
Dahang Wan China	6	188 0.5×	96 0.4×	41 0.6×	51 0.8×	21 0.4×	9	372
Guirong Weng China	17	690 1.8×	83 0.3×	27 0.4×	278 4.1×	24 0.5×	60	875
Xiang Yuan China	8	283 0.7×	60 0.2×	59 0.8×	101 1.5×	21 0.4×	21	500
Zhenhang Huang China	4	305 0.8×	46 0.2×	31 0.4×	77 1.1×	22 0.4×	4	498
Zheng Ge Japan	4	386 1.0×	53 0.2×	20 0.3×	55 0.8×	27 0.6×	5	496
Qimeng Wang China	5	686 1.8×	45 0.2×	28 0.4×	266 4.0×	36 0.7×	9	835

Countries citing papers authored by Linhui Dai

Since Specialization

Citations

This map shows the geographic impact of Linhui Dai'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 Linhui Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Linhui Dai more than expected).

Fields of papers citing papers by Linhui Dai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Linhui Dai. 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 Linhui Dai. The network helps show where Linhui Dai may publish in the future.

Co-authorship network of co-authors of Linhui Dai

This figure shows the co-authorship network connecting the top 25 collaborators of Linhui Dai. A scholar is included among the top collaborators of Linhui Dai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Linhui Dai. Linhui Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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8 of 8 papers shown

1.

Li, Yanshan, et al.. (2025). Small object detection network based on progressive enhanced multi-level feature fusion. Neurocomputing. 649. 130755–130755. 1 indexed citations

2.

Song, Pinhao, et al.. (2023). Boosting R-CNN: Reweighting R-CNN samples by RPN’s error for underwater object detection. Neurocomputing. 530. 150–164. 155 indexed citations breakdown →

3.

Chen, Yang, Pinhao Song, Hong Liu, et al.. (2023). Achieving domain generalization for underwater object detection by domain mixup and contrastive learning. Neurocomputing. 528. 20–34. 39 indexed citations

4.

Dai, Linhui, et al.. (2022). Enabling Trimap-Free Image Matting With a Frequency-Guided Saliency-Aware Network via Joint Learning. IEEE Transactions on Multimedia. 25. 4868–4879. 3 indexed citations

5.

Liu, Xiaohong, et al.. (2021). Video Frame Interpolation via Generalized Deformable Convolution. IEEE Transactions on Multimedia. 24. 426–439. 47 indexed citations

6.

Liu, Xiaohong, Chengqi Li, Linhui Dai, et al.. (2021). Learning for Unconstrained Space-Time Video Super-Resolution. IEEE Transactions on Broadcasting. 68(2). 345–358. 26 indexed citations

7.

Ding, Runwei, Linhui Dai, Guangpeng Li, & Hong Liu. (2019). TDD‐net: a tiny defect detection network for printed circuit boards. CAAI Transactions on Intelligence Technology. 4(2). 110–116. 277 indexed citations breakdown →

8.

Dai, Linhui, et al.. (2018). Robust image registration of printed circuit boards using improved SIFT‐PSO algorithm. The Journal of Engineering. 2018(16). 1793–1797. 11 indexed citations

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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