Dahang Wan

666 total citations · 1 hit paper
9 papers, 372 citations indexed

About

Dahang Wan is a scholar working on Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering and Aerospace Engineering. According to data from OpenAlex, Dahang Wan has authored 9 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 4 papers in Industrial and Manufacturing Engineering and 2 papers in Aerospace Engineering. Recurrent topics in Dahang Wan's work include Advanced Neural Network Applications (7 papers), Industrial Vision Systems and Defect Detection (4 papers) and Advanced Image and Video Retrieval Techniques (2 papers). Dahang Wan is often cited by papers focused on Advanced Neural Network Applications (7 papers), Industrial Vision Systems and Defect Detection (4 papers) and Advanced Image and Video Retrieval Techniques (2 papers). Dahang Wan collaborates with scholars based in China. Dahang Wan's co-authors include Rongsheng Lu, Xianli Lang, Siyuan Shen, Ting Xu, Bingtao Hu, Xu Liu, Deqiang Zhou, Yuzhong Zhang, Shuangbao Shu and Weiwei Fu and has published in prestigious journals such as Expert Systems with Applications, Sensors and Remote Sensing.

In The Last Decade

Dahang Wan

8 papers receiving 368 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.

Mixed local channel attention for object detection

2023 194 citations Dahang Wan, Rongsheng Lu et al. Engineering Applications of Artificial Intelligence profile →

Peers

Dahang Wan

CVPR

IME

AE

MT

EEE

Xianli Lang China

Lingyun Bi China

Haitong Lou China

Kin Wai Lau Hong Kong

Jie Lei China

Ren Qi-liang China

Lixia Deng China

Bharat Mahaur India

Shasha Li China

Xianli Lang China

Dahang Wan

215 ×1.1

CVPR

126 ×1.3

IME

97 ×1.6

AE

56 ×1.1

MT

53 ×1.3

EEE

Citations per year, relative to Dahang Wan Dahang Wan (= 1×) peers Xianli Lang

Countries citing papers authored by Dahang Wan

Since Specialization

Citations

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

Fields of papers citing papers by Dahang Wan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dahang Wan

This figure shows the co-authorship network connecting the top 25 collaborators of Dahang Wan. A scholar is included among the top collaborators of Dahang Wan 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 Dahang Wan. Dahang Wan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Wan, Dahang, Rongsheng Lu, Bingtao Hu, et al.. (2024). YOLO-MIF: Improved YOLOv8 with Multi-Information fusion for object detection in Gray-Scale images. Advanced Engineering Informatics. 62. 102709–102709. 49 indexed citations

2.

Zhou, Deqiang, et al.. (2024). C2DEM-YOLO: improved YOLOv8 for defect detection of photovoltaic cell modules in electroluminescence image. Nondestructive Testing And Evaluation. 40(1). 309–331. 30 indexed citations

3.

Ren, Zhijie, et al.. (2024). YOLO-SDH: improved YOLOv5 using scaled decoupled head for object detection. International Journal of Machine Learning and Cybernetics. 16(3). 1643–1660. 4 indexed citations

4.

Lu, Rongsheng, et al.. (2024). Bicrack: a bilateral network for real-time crack detection. International Journal of Machine Learning and Cybernetics. 16(5-6). 3097–3112.

5.

Shen, Siyuan, et al.. (2024). Real-Time 3-D Measurement With Dual-Frequency Fringes by Deep Learning. IEEE Sensors Journal. 24(10). 16576–16586. 1 indexed citations

6.

Wan, Dahang, et al.. (2023). Random Interpolation Resize: A free image data augmentation method for object detection in industry. Expert Systems with Applications. 228. 120355–120355. 17 indexed citations

7.

Wan, Dahang, et al.. (2023). YOLO-HR: Improved YOLOv5 for Object Detection in High-Resolution Optical Remote Sensing Images. Remote Sensing. 15(3). 614–614. 53 indexed citations

8.

Wan, Dahang, et al.. (2023). Mixed local channel attention for object detection. Engineering Applications of Artificial Intelligence. 123. 106442–106442. 194 indexed citations breakdown →

9.

Lang, Xianli, et al.. (2022). MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects. Sensors. 22(24). 9897–9897. 24 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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