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Image Recognition Method of Brake Beam Pillar Broken Fault of Railway Freight Car

A railway freight car and image recognition technology, which is applied in image analysis, image enhancement, image data processing, etc., can solve the problems of low detection accuracy and poor stability, and achieve the effect of unified operation standards, high accuracy, and fast speed

Active Publication Date: 2020-09-04
HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the problem of low accuracy and poor stability of the fault detection of the existing railway freight car brake beam pillar fault, and propose a fault image recognition method for the broken fault of the railway freight car brake beam pillar

Method used

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  • Image Recognition Method of Brake Beam Pillar Broken Fault of Railway Freight Car
  • Image Recognition Method of Brake Beam Pillar Broken Fault of Railway Freight Car
  • Image Recognition Method of Brake Beam Pillar Broken Fault of Railway Freight Car

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Experimental program
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specific Embodiment approach 1

[0029] Embodiment 1: The specific process of the image recognition method for the fractured fault of the brake beam strut of a railway freight car in this embodiment is as follows:

[0030] Step 1. Create a sample data set;

[0031] Step 2: Based on the sample data set, find the optimal weight coefficient to obtain a trained SSD deep learning network; the specific process is as follows:

[0032] Step 21: Initialize the weight coefficient in a random way;

[0033] Step 22: Perform multi-scale feature extraction on the sample data set to increase the receptive field; SSD is a multi-scale feature map detection network structure, which extracts feature maps of different scales for detection. feature maps) can be used to detect small objects, while small-scale feature maps (later feature maps) are used to detect large objects. The receptive fields included in the feature map selected by the model are: 38, 19, 10, 5, 3, 1. For each feature map, four offset positions and N categor...

specific Embodiment approach 2

[0040] Embodiment 2: The difference between this embodiment and Embodiment 1 is that a sample data set is established in the first step; the specific process is:

[0041] Build high-definition equipment around the truck tracks respectively. After the truck passes through the equipment, obtain a full range of high-definition images on both sides, bottom and upper part of the truck; the image is a clear grayscale image; because the parts of the truck may be affected by rain, mud, oil, and white paint , black paint, foreign objects, ice and snow, chalk and other natural or man-made conditions. Also, there may be differences in images taken at different sites. As a result, the brake beam strut images vary widely. Therefore, in the process of collecting the image data of the brake beam strut, it is necessary to ensure the diversity, and try to collect all the brake beam strut images under various conditions.

[0042] In the truck bogies of different models, the shape of the brake...

specific Embodiment approach 3

[0053] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that the default box (anchor box) is generated in the steps 2 and 3; the specific process is:

[0054] For each feature map, generate k default boxes (default boxes [default boxes]) according to different sizes (scales) and aspect ratios (ratios);

[0055] The formula for calculating the size of each default box is:

[0056] Among them, m is the number of feature maps, s min is the size of the bottommost feature map, s max Default box size for the topmost feature map;

[0057] The aspect ratio of each default box is calculated according to the scale value, and the scale value is {1, 2, 3, 1 / 2, 1 / 3};

[0058] For the default box with a scale of 1, add an additional scale of the default box;

[0059] In the formula, s' k For the increased ratio ({1,2,3,1 / 2,1 / 3} there are a total of 5 default boxes (one number represents one), this is the size of the increased default box, 5+1=6 default bo...

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Abstract

The invention relates to a fault image recognition method for a broken railway freight car brake beam pillar, and the invention relates to a fault image recognition method for a railway freight car. The purpose of the invention is to solve the problems of low detection accuracy and poor stability of the existing railway freight car brake beam pillar fault detection. The process is: 1. Establish a sample data set; 2. Find the optimal weight coefficient based on the sample data set to obtain a trained SSD deep learning network; the process is: initialize the weight coefficient in a random manner; perform feature extraction on the sample data set ;default frame generation; mark preprocessing; SSD target loss function; optimize the weight through the optimizer Adam; after the loss function and the optimizer, calculate the new weight coefficient, repeat the execution, and get the trained SSD deep learning network. 3. Input the real passing car image into the trained SSD deep learning network to identify the breakage of the brake beam pillar. The invention is used in the field of fault image recognition.

Description

technical field [0001] The invention relates to a method for identifying fault images of railway freight cars. Background technique [0002] During train operation, accidents caused by brake failures account for a large proportion of the entire road, and are inertial accidents in the vehicle sector. In light cases, the brake beam and pull-down rod fall off, which constitutes a dangerous accident; in severe cases, the driving equipment is damaged. Cause the train to derail and overturn, which may constitute a major traffic accident. Because the brake beam struts are not well manufactured and assembled, etc., the breakage of the brake beam struts is a fault that accounts for a high proportion of braking failures. Brake beam strut fracture is a kind of failure that endangers driving safety. In brake beam strut fault detection, manual inspection of images is used for fault detection. Due to the fact that the inspectors are prone to fatigue and omissions during their work, resu...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0006G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/30164G06F18/24
Inventor 刘丹丹
Owner HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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