Daniel Seichter

643 citations
19 papers · 472 · 1 hit paper · h-index 9

Impact in

Papers in

Daniel Seichter

17 papers receiving 460 citations

Daniel Seichter's Hit Papers

How to get pavement distress detection ready for deep learning? A systematic approach 2017 · 309 citations
3090+3+6Years since publication100200300

Peers

Daniel Seichter
Comparison fields: 5 of 37
  • Civil and Structural Engineering 336
  • Computer Vision and Pattern Recognition 116
  • Industrial and Manufacturing Engineering 55
  • Geology 23
  • Ocean Engineering 51
Replace Karl Amende with:
Karl Amende Germany
Mohamed Abdellatif Egypt
Fen Fang Singapore
Liangfu Ge China
Harriet Peel United Kingdom
Xiaoxi Gong China
Nachuan Ma China
Jianwei Liu China
Wensheng Su China
Dehua Wei China
Daniel Seichter relative to Karl Amende Germany Karl Amende's profile →
Citations per field
00.5×1.5×
Karl Amende · 1×
Citations per year

Countries citing papers authored by Daniel Seichter

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Seichter

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 11 scholars most cited alongside Daniel Seichter, 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 Daniel Seichter Line = papers co-authored together Daniel Seichter links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1
How to get pavement distress detection ready for deep learning? A systematic approach
Hit paper breakdown →
2017309
2 202231
3 201922
4 202121
5 201614
6 201813
7 201611
8 20199
9 20239
10 20197
11 20207
12 20235
13
Enhancing the Quality of Visual Road Condition Assessment by Deep Learning
20195
14 20213
15 20203
16
Speeding up Deep Neural Networks on the Jetson TX1
20182
17 20251
18 20240
19 20240

About Daniel Seichter

Daniel Seichter is a scholar working on Computer Vision and Pattern Recognition, Civil and Structural Engineering, Aerospace Engineering, Control and Systems Engineering and Signal Processing, having authored 19 papers that have together received 472 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (8 papers), Human Pose and Action Recognition (6 papers), Advanced Neural Network Applications (6 papers), Infrastructure Maintenance and Monitoring (4 papers), Advanced Image and Video Retrieval Techniques (4 papers), Robotics and Sensor-Based Localization (4 papers), Asphalt Pavement Performance Evaluation (3 papers) and Industrial Vision Systems and Defect Detection (2 papers). The work is most often cited by research in Civil and Structural Engineering (336 citations), Computer Vision and Pattern Recognition (116 citations), Industrial and Manufacturing Engineering (55 citations), Geology (23 citations) and Ocean Engineering (51 citations). Daniel Seichter has collaborated with scholars based in Germany and Austria. Frequent co-authors include Horst–Michael Groß, Markus Eisenbach, Ronny Stricker, Klaus Debes, Karl Amende, Steffen Müller, Matthias Hahn, Joachim Wagner, Denise Günther and Andrea Scheidig. Their work appears in journals such as International Journal of Social Robotics, Robotics and Autonomous Systems, Lecture notes in computer science, Common Library Network (Der Gemeinsame Bibliotheksverbund) and Fraunhofer-Publica (Fraunhofer-Gesellschaft).

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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