Railway wagon swing bolster spring jump-out fault image recognition method

A technology for railway wagons and image recognition, applied in image enhancement, image analysis, image data processing and other directions, can solve problems such as low detection accuracy, achieve uniform operating standards, improve detection efficiency and accuracy, and reduce the number of false alarms. Effect

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

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Problems solved by technology

[0003] The purpose of the present invention is to propose a fault image recognition method for bolster springs of railway wagons in view of the low detection accuracy of manual detection methods in the prior art

Method used

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  • Railway wagon swing bolster spring jump-out fault image recognition method
  • Railway wagon swing bolster spring jump-out fault image recognition method
  • Railway wagon swing bolster spring jump-out fault image recognition method

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specific Embodiment approach 1

[0031] Specific implementation mode one: refer to Figure 1 to Figure 3 Specifically explaining this embodiment, a method for image recognition of a fault image of a bolster spring of a railway freight car described in this embodiment includes the following steps:

[0032] Step 1: Obtain the image of the passing truck and perform rough positioning to obtain a rough positioning grayscale image set;

[0033] Step 2: mark the rough positioning grayscale image set to obtain the marked image set;

[0034] Step 3: Use the labeled image set and the original image to train the deep learning network model;

[0035] Step 4: Use the trained deep learning network model to segment the bolster spring, and judge whether there is a fault of the bolster spring according to the segmentation result. The judgment process is as follows:

[0036] First, the image is predicted using the deep learning network model, and the trained weight coefficient is used to predict the contour area of ​​the bol...

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Abstract

The invention discloses a railway wagon swing bolster spring jump-out fault image recognition method, relates to the technical field of freight train detection, and aims to solve the problem of low detection accuracy of a manual detection mode in the prior art. The method comprises the following steps: 1, obtaining a passing wagon image, and carrying out the coarse positioning to obtain a coarse positioning gray image set; 2, marking the coarse positioning grayscale image set to obtain a marked image set; 3, training a deep learning network model by using the marked image set and the originalimage; and 4, segmenting the swing bolster spring by using the trained deep learning network model, and judging whether a swing bolster spring jump-out fault occurs or not according to a segmentationresult. According to the invention, manual detection is replaced by an image automatic recognition mode, vehicle faults can be automatically identified and alarmed, the operation standard is unified,the influence of different experience, understanding and cognition degrees of detected vehicle personnel is avoided, and the detection efficiency and accuracy are improved.

Description

technical field [0001] The invention relates to the technical field of freight train detection, in particular to a fault image recognition method for bolster springs of railway freight cars. Background technique [0002] Railway freight car bolster springs are used in the running part of railway vehicles to play the role of buffering and shock absorbing. Because they are located in the running part, if they break, they will directly endanger the safety of train running. If the bolster spring breaks out, it will cause the car body to tilt. A fault that endangers driving safety, in the fault detection of bolster spring, the fault detection is carried out by manually checking the image. Due to the fact that the inspectors are prone to fatigue and omissions during the work process, resulting in missed inspections and wrong inspections, which affect driving safety. The method of automatic image recognition can improve the detection efficiency and stability. In recent years, dee...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0004G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/30108G06F18/241
Inventor 刘丹丹
Owner HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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