Name
Abstract | ||
|---|---|---|
Rail inspection is one of the most important tasks to guarantee the safety of a railway transportation system, and it requires advanced information technologies (e.g. cyber–physical system and cyber–physical–social system) to build intelligent inspection systems. This paper presents a cyber-enabled visual inspection system for rail corrugation, which includes an on-board image acquisition subsystem and a corrugation identification subsystem. In the corrugation identification subsystem, a track image captured by the on-board image acquisition subsystem is first segmented by the rail locating algorithm based on weighted projection profile (briefly as RLWP). And then each column of the segmented rail image is represented by local frequency features and identified as corrugation line or not by a support vector machine (SVM). Lastly, the rail image is judged as corrugation by integrating the recognized corrugation lines. The experiment results show that RLWP is robust and accurate to localize rail region even for uneven or abominable illumination. Moreover, the precision and recall of the proposed corrugation detection system are 98.47% and 96.50%, respectively. They are 25% and 1% higher than those of traditional methods. At the same time, the detection speed is doubly faster than that of the traditional approach. c |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.future.2017.04.032 | Future Generation Computer Systems |
Keywords | Field | DocType |
Corrugation,Rail inspection,Support vector machine,Local frequency feature,Cyber–physical System,Cyber–physical–social system | Rail inspection,Computer vision,Visual inspection,Computer science,Information technology,Precision and recall,Support vector machine,Real-time computing,Cyber-physical system,Artificial intelligence,Railway transportation | Journal |
Volume | Issue | ISSN |
79 | P1 | 0167-739X |
Citations | PageRank | References |
2 | 0.38 | 11 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingyong Li | 1 | 162 | 25.38 |
| Zhiping Shi | 2 | 168 | 43.86 |
| Huayan Zhang | 3 | 36 | 5.32 |
| Yunqiang Tan | 4 | 4 | 1.44 |
| Shengwei Ren | 5 | 49 | 3.10 |
| Peng Dai | 6 | 2 | 2.75 |
| Weiyi Li | 7 | 2 | 0.72 |