A machine vision system for inspection of railway track

Vasin, Nikolay N.; Diyazitdinov, Rinat R.

doi:10.18287/2412-6179-2016-40-3-410-415

Cited by 5 publications

(3 citation statements)

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“…The schematic diagram is shown in Figure 4 below. The algorithm uses a normalized correlation coefficient to measure the similarity, 20,21 and its calculation formula is shown in the following formula (5):…”

Section: Accurate Positioning Of Fastenersmentioning

confidence: 99%

“…In recent years, many researchers have made some efforts to apply vision-based technology to the detection tasks of railway fasteners. 5,6 However, if the original image taken on the track line is used for detection, because the original track image area is too large, which will lead to poor real-time detection results due to long recognition calculation time, and the complex background in the original track image will lead to the decrease of detection accuracy. To solve these problems, research on the track fastener positioning is necessary for the fastener fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Research on track fastener positioning method based on local unidirectional template matching

Chen

et al. 2021

Science Progress

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Commonly used fastener positioning methods include pixel statistics (PS) method and template matching (TM) method. For the PS method, it is difficult to judge the image segmentation threshold due to the complex background of the track. For the TM method, the search in both directions of the global is easily affected by complex background, as a result, the locating accuracy of fasteners is low. To solve the above problems, this paper combines the PS method with the TM method and proposes a new fastener positioning method called local unidirectional template matching (LUTM). First, the rail positioning is achieved by the PS method based on the gray-scale vertical projection. Then, based on the prior knowledge, the image of the rail and the surrounding area of the rail is obtained which is referred to as the 1-shaped rail image; then, the 1-shaped rail image and the produced offline symmetrical fastener template is pre-processed. Finally, the symmetrical fastener template image is searched from top to bottom along the rail and the correlation is calculated to realize the fastener positioning. Experiments have proved that the method in this paper can effectively realize the accurate locating of the fastener for ballastless track and ballasted track at the same time.

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Section: Accurate Positioning Of Fastenersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on track fastener positioning method based on local unidirectional template matching

Chen

et al. 2021

Science Progress

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“…In [16] developed design of the device for measurement of the depth of the surface defect of the controlled surface, for example. Vasin [17] describe a machine vision system for the inspection of the railway track condition.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in the problem of recognition of pitting corrosion on aluminum surfaces

Enikeev

Enikeeva

Малеева

et al. 2018

Collection of Selected Papers of the IV International Conference on Information Technology and Nanotechnology

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The work is devoted to the problem of identification and quantitative estimation of pitting corrosion. The purpose of the work is to design an algorithm and create a set of programs for evaluating and predicting corrosion processes development on the aluminum surface. Object of a research is process of corrosion of aluminum with hydrogen depolarization. Results of work can be used for a research of corrosion processes and their mechanisms on the basis of the visual analysis.

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