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Reeling steel state identifying method based on KNN reeling steel picture data of optimized KAP sample

A technology of state classification and identification methods, applied in character and pattern recognition, instruments, computer parts, etc.

Active Publication Date: 2014-03-12
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0004] In order to avoid the deficiencies of the prior art, the present invention proposes a method for classifying and identifying the steel coil state based on KNN coil steel picture data optimized by KAP samples, and solves the classification problem of automatically identifying the coil steel loading state under complex natural conditions

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  • Reeling steel state identifying method based on KNN reeling steel picture data of optimized KAP sample
  • Reeling steel state identifying method based on KNN reeling steel picture data of optimized KAP sample
  • Reeling steel state identifying method based on KNN reeling steel picture data of optimized KAP sample

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Embodiment Construction

[0044] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0045] The hardware environment that is used for implementing: Intel (R) Core (TM) i5-2300CPU2.80GHz2.80GHz RAM4.00GB operating software environment is Matlab2012a WINOWS7. We have realized the method that the present invention proposes with Matlab.

[0046] The concrete implementation process of the present invention is as follows:

[0047] Step 1. Get pictures. Using a linear array CCD camera, the real-time resolution of each vehicle is not less than 7000*2048 pixels, and the accuracy of each pixel is 2mm. see picture example Figure 7 ;

[0048] Step 2. Construct the characteristic sample space of the coil steel loading state. It mainly includes the following three steps:

[0049] a. Extract the SURF feature vector set. According to the volume picture obtained in step 1, the different loading states are set as NC (NC=3), expressed as

[0050] {C i |i=1...

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Abstract

The invention relates to a reeling steel state identifying method based on KNN reeling steel picture data of an optimized KAP sample, wherein KAP belongs to a fast efficient clustering algorithm, clustering is performed by the mutual relation among computation features, a new sample space is formed by clustering and selecting feature samples with representation in various samples, and the balance among each sample number in the new sample space is ensured by fixing the parameter K in the KAP algorithm. A PCA algorithm is utilized for performing dimension reducing on the features and the operation speed of the method is improved under the condition of ensuring the classifying precision. The classifying identification on the linear array CCD (charge couple device) picture-based reeling steel loading state under the complex natural environment is realized. The method has very high classifying precision and lower wrong classifying rate, and the actual needs are met with the faster classifying speed.

Description

technical field [0001] The invention belongs to the field of image processing and pattern recognition, and relates to a method for classifying and identifying coil rigid state based on KNN coil steel image data optimized by KAP samples, and classifies and recognizes coil rigid state in railway cargo inspection. Background technique [0002] Cargo loading reinforcement is an important measure to ensure the safety of railway operation and cargo safety. The train operation is in a dynamic state. If the loading and reinforcement are not good, the goods will move, roll, overturn or fall, collapse, and even cause the train to overturn. Among all kinds of cargo, coiled steel is currently one of the cargoes with the largest proportion of freight and the highest risk factor in transportation. At present, the loading and transportation of coiled steel mainly relies on the detection of the overload and eccentric load system, and the method is relatively simple. In view of the fact tha...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/66
Inventor 俞大海韩军伟王东阳郭雷
Owner NORTHWESTERN POLYTECHNICAL UNIV
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