Railway wagon floor damage fault image identification method

A technology for image recognition and railway wagons, which is applied in image enhancement, image analysis, image data processing, etc. It can solve the problems of low detection rate, fatigue and omission of vehicle inspectors, and achieve easy training, improved recognition recall rate, The effect of improving detection efficiency

Active Publication Date: 2020-04-28
HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem of low detection rate due to the fact that inspection personnel are prone to fatigue and omissions during the work process by manually inspecting images in the prior art, and propose a broken floor of railway freight cars Fault Image Recognition Method

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  • Railway wagon floor damage fault image identification method
  • Railway wagon floor damage fault image identification method
  • Railway wagon floor damage fault image identification method

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specific Embodiment approach 1

[0040] Specific Embodiment 1: This embodiment will be described in detail with reference to the figure. A method for image recognition of damaged fault images on the floor of railway wagons described in this embodiment includes the following steps:

[0041] Step 1: Obtain high-definition line scan images of passing trucks;

[0042] Step 2: Cut out the area of ​​the part to be recognized from the image according to prior knowledge, and establish a sample data set;

[0043] Step 3: Perform data amplification on the sample data set;

[0044] Step 4: Mark the images in the dataset;

[0045] Step 5: Generate a data set from the original image and labeled data, and train the model;

[0046] Step 6: Use the SEGNET-UNET network to segment the image, and mark each segmented part;

[0047] Step 7: For the floor segmentation result, divide the image into multiple fault areas according to the contour information. For each fault area, judge whether there is a floor damage fault accordin...

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Abstract

The invention discloses a railway wagon floor damage fault image identification method, relates to the technical field of freight train detection, and aims to solve the problem of low detection rate caused by fatigue and omission of train detection personnel in the working process when fault detection is carried out by adopting a manual image detection mode in the prior art; the method replaces manual detection with an automatic image recognition mode and improves the detection efficiency and accuracy. A deep learning algorithm is applied to automatic floor damage fault recognition, and the stability and precision of the overall algorithm are improved. In order to reduce the influence of rainy days on the recognition rate, except for a normal area and a damaged area, foreign matters such as a beam body area on the floor and weeds are marked respectively to improve the recognition accuracy. A U-NET model and the SEGNET model are combined to carry out fault identification. Compared withthe U-NET, the SEGNET-UNET has fewer parameters, so that the training is easier. Compared with the SEGNET, the SEGNET-UNET adds step-skipping connection by imitating the U-NET, and pays more attentionto details than the SEGNET, so that the boundary information can be better extracted.

Description

technical field [0001] The invention relates to the technical field of freight train detection, in particular to a fault image recognition method for floor damage of railway freight cars. Background technique [0002] The fault of truck floor damage is a common fault that endangers driving safety. It is characterized by a wide range of identification, complex background and changeable fault forms. At present, the dynamic vehicle inspection operation is still carried out manually by looking at pictures one by one. The main problems are as follows: due to the influence of personnel quality and sense of responsibility, errors and omissions occur from time to time, and the quality of the operation is difficult to guarantee; a large number of dynamic vehicle inspection personnel are required , low efficiency and huge labor costs. However, image processing and deep learning methods are used for automatic identification of floor damage faults, and manual confirmation of the alarm ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11
CPCG06T7/0004G06T7/11G06T2207/20081G06T2207/20084G06T2207/30164G06T2207/30204
Inventor 高恩颖
Owner HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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