Image recognition method for side frame fracture fault of railway freight car bogie

A technology for railway freight cars and image recognition, which is applied in image analysis, image data processing, character and pattern recognition, etc., can solve problems such as poor reliability and labor, and achieve the effect of improving accuracy and ensuring safe operation.

Active Publication Date: 2020-10-09
HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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Problems solved by technology

[0005] Aiming at the problem that the side frame fracture detection of the existing railway freight car bogie is carried out manually, and the reliability is poor, the present invention provides an image recognition method for the side frame fracture fault of the railway freight car bogie

Method used

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  • Image recognition method for side frame fracture fault of railway freight car bogie
  • Image recognition method for side frame fracture fault of railway freight car bogie
  • Image recognition method for side frame fracture fault of railway freight car bogie

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specific Embodiment approach 1

[0041] Specific implementation mode 1. Combination figure 1 As shown, the present invention provides a method for fault image recognition of railway freight car bogie side frame fracture fault, which includes the following steps:

[0042] Step 1: Collect the original grayscale image of the side frame of the truck bogie in operation, determine the side frame area of ​​each gray image according to the wheelbase information and position information of the truck bogie, and preprocess the side frame area to obtain A sample image of the side frame area, forming a sample image set of all the sample images of the side frame area, configuring tag information for each sample image of the side frame area to form a tag file, and forming a sample data set based on the sample image set and the tag file;

[0043] Step 2: using the sample data set to train the convolutional neural network inceptionv2 and the convolutional neural network Faster rcnn to obtain the trained inceptionv2 model and ...

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Abstract

The invention relates to a fault image recognition method for a side frame fracture of a railway freight car bogie, belonging to the technical field of railway freight car bogie safety. The invention aims at the problem that the side frame fracture detection of the existing railway freight car bogie is carried out manually, and the reliability is poor. Including collecting the original grayscale image of the sideframe of the truck bogie in operation, determining the sideframe area of ​​each grayscale image, preprocessing the sideframe area to obtain a sample image of the sideframe area, and forming a sample image of all the sample images of the sideframe area Set, configure the marking information for each side frame area sample image to form a marking file, and form a sample data set based on the sample image set and marking file; train the convolutional neural network inceptionv2 and the convolutional neural network Faster rcnn to obtain the trained inceptionv2 Model and Faster rcnn model; use the trained inceptionv2 model and Faster rcnn model to process the image to be detected, obtain the corresponding side frame state prediction results, and realize fault identification. The invention is used for the fracture identification of the bogie side frame.

Description

technical field [0001] The invention relates to a fault image recognition method for a side frame fracture of a railway freight car bogie, and belongs to the technical field of railway freight car bogie safety. Background technique [0002] The fracture fault of the side frame of the railway freight car bogie is a fault that endangers the traffic safety. If the fracture location cannot be dealt with in time before the fault occurs, safety accidents will easily occur. [0003] At present, in detecting the fracture of the side frame, it is necessary to obtain an image of the side frame first, and then manually detect the image to determine whether the side frame is broken. In the process of inspecting a large number of images, the inspectors are prone to human factors such as fatigue and omissions, which may cause missed inspections and wrong inspections. Therefore, the reliability and efficiency of this judgment method will be affected. Affect the driving safety of trucks. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/32G06K9/62G06N3/04G06T7/11
CPCG06T7/11G06V10/25G06N3/045G06F18/214
Inventor 付德敏
Owner HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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