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Dynamic image detection equipment and detection method for truck running faults

A technology of running faults and dynamic images, applied in the direction of railway vehicle testing, etc., can solve problems such as errors in detection results, and achieve the effects of improving accuracy, optimizing operation steps, and improving detection stability.

Active Publication Date: 2021-05-25
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 provide a dynamic image detection device and detection method for truck operation faults in order to overcome the problem of errors in image detection results collected only by high-speed industrial line array cameras

Method used

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  • Dynamic image detection equipment and detection method for truck running faults

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specific Embodiment approach 1

[0016] The dynamic image detection device for truck running faults of the present invention includes a computer, a wheel sensor, an automatic identification server and a 3D image acquisition module;

[0017] The device of the present invention adds a 3D image acquisition module based on the original TFDS-3 module, and the TFDS-3 module is a dynamic image detection system for truck running faults currently used.

[0018] The TFDS-3 module includes a line-scan camera. The line-scan camera only needs to scan a line when framing the view, so the viewfinder opening can be made very narrow. The influence of external environment such as wind, sand, rain and snow on the equipment is eliminated.

[0019] The TFDS-3 module consists of three parts: trackside equipment, detection station equipment, and train inspection center equipment.

[0020] 1) Railside equipment mainly includes: caisson, side box, junction box, car number antenna, wheel sensor, etc.

[0021] 2) The detection statio...

specific Embodiment approach 2

[0032] The difference between the second embodiment and the first embodiment is that step three is specifically as follows:

[0033] Step 31, using a deep learning model to process the grayscale image, or the 3D image, or the combination of the grayscale image and the 3D image, to identify the type of train;

[0034] Store reference grayscale images of different types of trains, reference 3D images, position marks of train components in the reference grayscale images and in the reference 3D images through a pre-established reference database; and according to the reference grayscale degree image and the reference 3D image training deep learning model for predicting different types of trains;

[0035] The reference grayscale image can be an image collected by a line scan camera and subjected to grayscale and binarization;

[0036] The training of the deep learning model is carried out through a certain number of reference grayscale images and reference 3D images collected in a...

specific Embodiment approach 3

[0044] The difference between the third embodiment and the first or second embodiment is that the 3D image includes a height image and an intensity image, and there is a complete mapping between the height image and the intensity image.

[0045] The image with the pixel gray value of the image of each channel is the intensity image, the intensity image may be a gray image, and the image intensity of the gray image is the pixel gray value.

[0046] The height image of the 3D image has three-dimensional coordinates, the intensity image has a pixel gray value, and each point of the height image corresponds to each point of the intensity image with a pixel gray value.

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Abstract

The truck fault dynamic image detection device and detection method of the present invention relate to a truck fault dynamic image detection system and a truck fault detection method, the purpose of which is to overcome the problem of errors in the detection results of images collected only by high-speed industrial line array cameras. The equipment includes a computer, a wheel sensor, an automatic identification server, and a 3D image acquisition module; the steps of the detection method using the equipment are as follows: Step 1, vehicle information collection; Step 2, image information collection; Step 3, fault identification; Step 4, fault deal with. The present invention adds 3D images to carry out image recognition on the basis of the vehicle inspection process of the dynamic image detection system for truck running faults. The detection stability and accuracy rate reach nearly 99%; and the automatic identification of faults is added, and the fatigue of the human eyes of the staff is greatly reduced.

Description

technical field [0001] The invention relates to a fault detection device and method for railway vehicles, in particular to a fault detection system based on a dynamic image of a freight train running fault and a fault detection method for a freight train. Background technique [0002] The current railway freight car inspection method uses a high-speed industrial line array camera combined with an infrared linear laser light source to collect images of running trains, analyzes and processes them through a computer, calculates the running speed of the train, and determines the type of train. This method distinguishes vehicle faults through the combination of man and machine, so as to achieve the purpose of dynamic detection of vehicle quality. [0003] However, the current vehicle inspection process is mainly based on the manual inspection of the vehicle inspector. When there are many trucks, the workload of the vehicle inspector will be very heavy. Coupled with the fatigue of...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M17/08
CPCG01M17/08
Inventor 霍连庆马凌宇张延明朱金良
Owner HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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