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Identification method for fracture fault images of swing bolsters of railway wagon

A railway freight car and image recognition technology, applied in image enhancement, image analysis, image data processing and other directions, can solve the problems of low detection accuracy and poor stability of railway freight car bolsters, reduce the impact of category imbalance, improve Accuracy, improved robustness and precision

Active Publication Date: 2020-05-01
HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem of low accuracy and poor stability of the existing railway freight car bolster fracture fault detection, and propose a railway freight car bolster fracture fault image recognition method

Method used

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  • Identification method for fracture fault images of swing bolsters of railway wagon
  • Identification method for fracture fault images of swing bolsters of railway wagon
  • Identification method for fracture fault images of swing bolsters of railway wagon

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Experimental program
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specific Embodiment approach 1

[0024] Specific implementation mode one: combine figure 1 , Figure 5 Describe this embodiment, the specific process of the image recognition method for the broken fault image of the railway freight car bolster in this embodiment is:

[0025] Step 1. Create a sample data set;

[0026] Step 2. Preliminary positioning of the area of ​​the bolster component;

[0027] Step 3. Carry out self-adaptive contrast enhancement to the regional image of the initially intercepted bolster parts, so that the brightness and darkness of the initially intercepted regional images of the bolster parts are the same;

[0028] Since the angular distance of the imaging equipment at each site is different, the brightness and darkness of the collected images are different. Some images are too dark to clearly observe the fracture area of ​​the bolster. Therefore, before entering the deep learning network, the image is adaptive to improve the contrast.

[0029] Step 4. Calculate the weight of the samp...

specific Embodiment approach 2

[0033] Embodiment 2: The difference between this embodiment and Embodiment 1 is that the sample data set is established in the step 1; the specific process is:

[0034] Imaging equipment is built on both sides of the railway track. After the truck passes through the equipment, a high-definition grayscale image is obtained; the image is a clear grayscale image. As truck components may be affected by rain, mud stains, oil stains, black paint and other natural or man-made conditions. Also, there may be differences in the images taken by different sites. Therefore, images of bolster parts vary widely. Therefore, in the process of collecting bolster image data, it is necessary to ensure diversity and try to collect all bolster images under various conditions.

[0035] In different types of bogies, the morphology of the bolster components varies. However, collection of bolster components for some less common bogie types is more difficult due to the wide variation in frequency bet...

specific Embodiment approach 3

[0042] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that in the step 2, the area of ​​the bolster component is initially positioned; the specific process is:

[0043] According to prior knowledge such as hardware equipment, wheelbase information and related positions, the area of ​​the bolster part is preliminarily intercepted from the image of the side camera.

[0044] Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

The invention discloses an identification method for fracture fault images of swing bolsters of a railway wagon and relates to a railway wagon fault image recognition method. The objective of the invention is to solve the problems of low accuracy and poor stability of existing railway wagon swing bolster fracture fault detection. The method comprises the steps of step 1, establishing a sample data set; step 2, carrying out initial positioning on an area of a swing bolster part; step 3, performing self-adaptive contrast improvement on the preliminarily intercepted area image of the swing bolster part to enable the preliminarily intercepted area image of the swing bolster part to have the same brightness degree; step 4, calculating the weight of the sample data set; and step 5, inputting areal vehicle passing image into a U-Dense type deep learning network, and based on the weight of the sample data set obtained in the step 4, determining the bolster fracture fault. The invention is applied to the field of bolster fracture fault image recognition.

Description

technical field [0001] The invention relates to a fault image recognition method for railway wagons. Background technique [0002] The bolster is the second largest steel casting in the running part of a railway vehicle (passenger car, freight car), and it is one of the important parts that directly affect the safety of train operation. According to the force of the bolster, the bending moment is the largest in the middle, so the bending stress is the largest, and the crack occurrence rate in the center of the bolster is the highest. [0003] The broken bolster fault of a truck is a kind of fault that endangers the driving safety. In the fault detection of the broken bolster, the fault detection is carried out by manually checking the image. Due to human factors such as fatigue and omissions, the inspectors are prone to fatigue and omissions during the work process, which may cause missed inspections and wrong inspections, which will affect driving safety. Contents of the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T5/00
CPCG06T7/0004G06T2207/20081G06T2207/20084G06T5/90
Inventor 付德敏
Owner HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD