Research on Sleeper Detection Scheme Based on Image and Laser Multi-data Fusion
Accurate detection of railway track sleepers is the premise to realize intelligent upgrade of tamping machine, and also one of the necessary conditions to realize the automatic tamping of large machines. In this paper, aiming at the problem of automatic detection and identification of track sleepers...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2021-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.01.004 |
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| Summary: | Accurate detection of railway track sleepers is the premise to realize intelligent upgrade of tamping machine, and also one of the necessary conditions to realize the automatic tamping of large machines. In this paper, aiming at the problem of automatic detection and identification of track sleepers for a large-scale tamping machine, an automatic detection algorithm for track sleepers based on multi-dimensional sensor data fusion algorithm is proposed. This scheme combines the chaos enhanced drosophila optimization algorithm (CFOA) and the fuzzy clustering segmentation technology, expands the application scope of the algorithm, improves the accuracy of image recognition and anti-noise performance, and carries out multi-data fusion with the laser ranging sensor data. At the same time, operating speed and braking characteristics of the tamping car are fully considered, and sleeper width and distance between sleepers are further determined. The experimental results of sleeper detection output signal show that the proposed detection algorithm can quickly and accurately give the position of rail sleeper, and judge the abnormal area, so as to lay the foundation for the subsequent automatic tamping operation control. |
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| ISSN: | 2096-5427 |