Steel rail tread defect recognition method based on combination of gray image and depth image

A defect recognition, grayscale image technology, applied in image enhancement, image analysis, image data processing and other directions, can solve the problems of large amount of 3D data and slow recognition speed, achieve high-precision defect recognition, low robustness, reduce The effect of false positives

Inactive Publication Date: 2018-11-30
BEIHANG UNIV
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Relying entirely on 3D data to realize recognition not only has the disadvantages of ...

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  • Steel rail tread defect recognition method based on combination of gray image and depth image
  • Steel rail tread defect recognition method based on combination of gray image and depth image

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[0037] The basic idea of ​​the present invention is: using the registered rail tread gray image and depth image, using the convolutional neural network, regression output the type distribution probability of the target defect and the position coordinates in the image, that is, to realize the defect recognition of the rail tread .

[0038] In the following, the present invention will be further described in detail in conjunction with specific embodiments and an end-to-end convolutional neural network that predicts the position and category of the output tread defect from the input registered rail tread gray image and depth image as an example.

[0039] Such as figure 1 As shown, the present invention is based on the rail tread defect recognition method that grayscale image and depth image combine and comprises the following steps:

[0040] Step 11: Build a dataset of rail tread images

[0041] The rail tread image dataset contains the registered rail tread gray image and dept...

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Abstract

The invention discloses a steel rail tread defect recognition method based on combination of a gray image and a depth image. The method comprises the steps of establishing a steel rail tread image data set by utilizing a registered gray image and depth image pair of a steel rail tread, dividing the data set into a training sample set and a test sample set, and preprocessing the images of the dataset; performing defect recognition by adopting a special convolutional neural network structure, wherein a front end of the network is provided with two branch structures, which can extract features from the gray image and the depth image of the steel rail tread respectively; fusing feature information of the gray image and the depth image through feature graph connection, and then outputting preliminary prediction results by adopting a prediction module; and finally, screening the preliminary defect prediction results by adopting a non-maximum suppression method to obtain a final steel rail defect recognition result. The method combines two-dimensional and three-dimensional features of the steel rail tread at the same time, has the capability of distinguishing real defects and pseudo-defects, can reduce the misjudgment rate and the missing detection rate, and is especially suitable for defect recognition in a complex environment.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and information processing, and relates to target detection technology, in particular to a rail tread defect recognition method based on the combination of grayscale images and depth images. Background technique [0002] Rail tread defect recognition refers to the process of locating the defect location and determining the defect type in the rail tread image. It is a difficult point in the application of computer vision technology in the industrial field, and it is an application technology of image-based target detection. Rail tread defect identification can be applied not only to the monitoring of the main railway line, but also to the detection of the rail production line. The difficulty in identifying rail tread defects lies in: affected by different factors such as light, water stains, and oil stains, the appearance of rail tread defects in the image is complex; some authentic an...

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Application Information

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IPC IPC(8): G06T7/00G06T7/30G06N3/04
CPCG06T7/0006G06T7/30G06T2207/30168G06T2207/20081G06N3/045
Inventor 刘震吴穗宁任一鸣胡杨李若铭
Owner BEIHANG UNIV
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