Daniel Seichter

624 citations
18 papers · 424 · 1 hit paper · h-index 8

Impact in

Papers in

Daniel Seichter

14 papers receiving 411 citations

Daniel Seichter's Hit Papers

How to get pavement distress detection ready for deep learning? A systematic approach 2017 · 281 citations
2810+3+6Years since publication50100150200250

Peers

Daniel Seichter
Comparison fields: 5 of 35
  • Civil and Structural Engineering 308
  • Computer Vision and Pattern Recognition 104
  • Industrial and Manufacturing Engineering 49
  • Geology 22
  • Ocean Engineering 49
Replace Mohamed Abdellatif with:
Mohamed Abdellatif Egypt
Fen Fang Singapore
Liangfu Ge China
Harriet Peel United Kingdom
Yongzhi Min China
Xiaoxi Gong China
Wensheng Su China
Jianwei Liu China
Nachuan Ma China
Shiyan Wang China
Daniel Seichter relative to Mohamed Abdellatif Egypt Mohamed Abdellatif's profile →
Citations per field
00.5×3.5×
Mohamed Abdellatif · 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

18 of 18 papers shown
#Work
1
How to get pavement distress detection ready for deep learning? A systematic approach
Hit paper breakdown →
2017281
2 202230
3 201921
4 202120
5 201612
6 201811
7 201610
8 20198
9 20237
10 20207
11 20196
12 20234
13
Enhancing the Quality of Visual Road Condition Assessment by Deep Learning
20194
14 20202
15
Speeding up Deep Neural Networks on the Jetson TX1
20181
16 20240
17 20240
18 20250

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 Biomedical Engineering, having authored 18 papers that have together received 424 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (7 papers), Human Pose and Action Recognition (6 papers), Advanced Neural Network Applications (6 papers), Robotics and Sensor-Based Localization (4 papers), Advanced Image and Video Retrieval Techniques (4 papers), Infrastructure Maintenance and Monitoring (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 (308 citations), Computer Vision and Pattern Recognition (104 citations), Industrial and Manufacturing Engineering (49 citations), Geology (22 citations) and Ocean Engineering (49 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, Denise Günther, Yvonne Steffen and Majon Muller. Their work appears in journals such as Robotics and Autonomous Systems, International Journal of Social Robotics, SHILAP Revista de lepidopterología, 2022 International Joint Conference on Neural Networks (IJCNN) 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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