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Image quality judgment method for sub-task railway wagons based on sparse representation and svm

A quality judgment and sparse representation technology, applied in the field of image processing, can solve problems such as low accuracy rate and failure, and achieve the effect of improving accuracy rate, reducing difficulty, and ensuring efficiency

Active Publication Date: 2021-02-02
HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention aims to solve the problem of low accuracy or even failure when the existing image quality judgment method detects truck images

Method used

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  • Image quality judgment method for sub-task railway wagons based on sparse representation and svm
  • Image quality judgment method for sub-task railway wagons based on sparse representation and svm
  • Image quality judgment method for sub-task railway wagons based on sparse representation and svm

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Experimental program
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specific Embodiment approach 1

[0054] Specific implementation mode one: combine figure 2 To describe this embodiment,

[0055] The method for judging the image quality of sub-task railway wagons based on sparse representation and SVM described in this embodiment includes the following steps:

[0056] 1. Collect image disease and establish image quality judgment data set:

[0057] Set up high-definition cameras on both sides and bottom of the railway wagon. For example, two cameras can be installed on each side of the wagon, and three cameras can be installed on the bottom to obtain the image of the wagon after the wagon passes; The impact of the captured images will be of low quality, which will seriously affect the subsequent recognition tasks. Therefore, it is necessary to judge the image quality before fault detection to improve the accuracy of fault detection.

[0058] The present invention judges the quality of the image by task, respectively evaluates whether the brightness, clarity, symmetry, str...

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Abstract

The invention discloses a method for judging the image quality of railway wagons with sub-tasks based on sparse representation and SVM, and belongs to the technical field of image processing. In order to solve the problem that the existing image quality judgment method has low accuracy or even failure when detecting the truck image. In the present invention, the truck image is sent to the trained quality judgment network to obtain the image quality judgment result; in the process of determining the quality judgment network: five positive samples and negative samples are constructed according to the brightness, clarity, symmetry, stretching degree and noise. The quality judgment data set and the image quality score of each image are used as quality labels; for the five quality judgment tasks, different features of the image are extracted and encoded using sparse representation, and different quality judgment models are constructed and trained respectively. A quality judgment model obtains five quality scores to determine the final quality score as the judgment result of image quality judgment. It is mainly used to judge the image quality of railway wagons.

Description

technical field [0001] The invention relates to a method for judging the image quality of railway wagons. It belongs to the technical field of image processing. Background technique [0002] Railway wagons have always played an important role in transportation, and the railway department needs to conduct safety inspections on railway wagons frequently to ensure the safe and stable operation of railway wagons. For a long time, the equipment inspection of railway freight cars has basically used the method of manually viewing images to inspect the entire freight train, which has always had problems such as high cost and low efficiency. Using the method of manually viewing images is boring, easy to slack, effective working time is limited, efficiency is further reduced, and it is very easy to miss parts, false alarms, etc., and it is difficult to guarantee accuracy. [0003] Compared with the fault detection method of manually checking images, the method of automatic fault det...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06K9/46G06K9/62B61K9/00
CPCG06T7/0002B61K9/00G06T2207/30168G06V10/40G06V10/513G06F18/2411
Inventor 韩旭
Owner HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD