Deegan Atha

616 citations
11 papers · 446 · 1 hit paper · h-index 7

Impact in

Papers in

Deegan Atha

11 papers receiving 433 citations

Deegan Atha's Hit Papers

Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection 2017 · 276 citations
2760+3+6Years since publication50100150200250

Peers

Deegan Atha
Comparison fields: 5 of 60
  • Civil and Structural Engineering 213
  • Computer Vision and Pattern Recognition 122
  • Geology 33
  • Industrial and Manufacturing Engineering 45
  • Astronomy and Astrophysics 60
Replace Yoji KURODA with:
Yoji KURODA Japan
Giuseppe Del Core Italy
P. Bellutta United States
Qijin Chen China
Qingwen Wu China
Dong Seop Han China
Oliver Heirich Germany
Damir Vučina Croatia
Xinxiang Zhang United States
Fen Fang Singapore
Deegan Atha relative to Yoji KURODA Japan Yoji KURODA's profile →
Citations per field
00.5×10×16.8×
Yoji KURODA · 1×
Citations per year

Countries citing papers authored by Deegan Atha

Since Specialization
Citations

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

Fields of papers citing papers by Deegan Atha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 24 scholars most cited alongside Deegan Atha, 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 Deegan Atha Line = papers co-authored together Deegan Atha links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1
Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection
Hit paper breakdown →
2017276
2 202167
3 202232
4 202418
5 202217
6 202314
7 202211
8 20226
9 20243
10 20241
11 20181

About Deegan Atha

Deegan Atha is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Astronomy and Astrophysics, Automotive Engineering and Civil and Structural Engineering, having authored 11 papers that have together received 446 indexed citations. Recurring topics across this work include Planetary Science and Exploration (4 papers), Robotics and Sensor-Based Localization (4 papers), Autonomous Vehicle Technology and Safety (3 papers), Advanced Neural Network Applications (3 papers), Robotic Path Planning Algorithms (2 papers), Astro and Planetary Science (2 papers), Space Science and Extraterrestrial Life (1 paper) and Infrastructure Maintenance and Monitoring (1 paper). The work is most often cited by research in Civil and Structural Engineering (213 citations), Computer Vision and Pattern Recognition (122 citations), Geology (33 citations), Industrial and Manufacturing Engineering (45 citations) and Astronomy and Astrophysics (60 citations). Deegan Atha has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Mohammad R. Jahanshahi, Masahiro Ono, Matthew Gildner, Henry A. Leopold, Marco Hutter, Larry Matthies, Shreyansh Daftry, Marko Bjelonic, Barry Ridge and Lorenz Wellhausen. Their work appears in journals such as IEEE Robotics and Automation Letters, Structural Health Monitoring, Purdue e-Pubs (Purdue University), 2022 IEEE Aerospace Conference (AERO) and 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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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