CN105354596A - Structured sparse coding based coal rock identification method - Google Patents

Abstract

The invention discloses a structured sparse coding based coal rock identification method. According to the method, spatial structure features of coal rocks are captured, so that the method has very good discrimination capability and robustness to imaging environment change. Therefore, the method has very high identification stability and identification correctness and can provide reliable coal rock identification information for production processes of automated mining, automated coal discharge, automated waste rock selection and the like.

Description

The Coal-rock identification method of structure based sparse coding

Technical field

The present invention relates to a kind of Coal-rock identification method of structure based sparse coding, belong to coal and rock identify field.

Background technology

Namely coal and rock identify automatically identifies coal petrography object by a kind of method is coal or rock.In coal production process, coal and rock identify technology can be widely used in cylinder coal mining, driving, top coal caving, raw coal select the production links such as spoil, for minimizing getting working face operating personnel or realize unmanned operation, alleviate labor strength, improve operating environment, to realize mine safety High-efficient Production significant.

Multiple method is had to be applied to coal and rock identify, as natural Gamma ray detection, radar detection, stress pick, infrared acquisition, active power monitoring, shock detection, sound detection, dust detection, memory cut etc., but there is following problem in these methods: 1. need to install various kinds of sensors obtaining information on existing additional, cause apparatus structure complicated, cost is high.2. the equipment such as coal mining machine roller, development machine in process of production stressed complexity, vibration is violent, serious wear, dust large, sensor deployment is more difficult, and easily cause mechanical component, sensor and electric wiring to be damaged, device reliability is poor.3. for dissimilar plant equipment, there is larger difference in the selection of the best type of sensor and picking up signal point, needs to carry out personalized customization, the bad adaptability of system.

For solving the problem, image technique also more and more comes into one's own and have developed some Coal-rock identification method based on image technique, but existing method is all carry out coal and rock identify with the characteristics of image of human subjective's design or the combination of characteristics of image, the feature of engineer often can not accurately be caught coal petrography image essential structure to cause not have tool robustness to changing the view data change caused because of image-forming condition, thus identifying that stability and recognition correct rate also have very large deficiency.

Need a kind of Coal-rock identification method solving or at least improve one or more problems intrinsic in prior art, to improve coal and rock identify rate and to identify stability.

Summary of the invention

Therefore, the object of the present invention is to provide a kind of Coal-rock identification method of structure based sparse coding, the method captures the airspace structure feature of coal petrography, thus there is very strong distinguishing ability and the robustness to imaging circumstances change, thus make the method have very high identification stability and recognition correct rate, the production runes such as cash can be selected to provide reliable coal and rock identify information for automated mining, automatic coal discharge, robotization.

According to a kind of embodiment form, a kind of Coal-rock identification method of structure based sparse coding is provided, comprises the steps:

A. the image that M+1 opens known coal (or rock) object is gathered;

B. N number of image block (M+1) N number of image block [x is altogether extracted from often opening image respectively ₁, x ₂... x _{(M+1) N}]=X ∈ R ^{p × (M+1) N}, p is the dimension after image block vectorization;

C. the image block x that wherein M opens image is got _ias the primitive matrix D=[d of coal (or rock) image ₁, d ₂... d _mN] ∈ R ^{p × MN};

D. by separating optimization problem obtain each image block x of the remaining figure expressed with D _iprimitive coefficient b _i∈ R ^{mN × 1}, image block x _ifeature be and image block x _ithe average of the primitive coefficient of same position is namely: wherein C is normaliztion constant, all image block x _istructural feature matrix U, characteristics of image gets this matrix diagonals element, i.e. F=diag (U);

E. for coal petrography image to be identified, N number of image block y is extracted by the method identical with step B _i, express image block y with the D in step C _i, obtain characteristics of image V to be identified by the method identical with step D;

F. use the similarity of tolerance and known coal or rock object, be coal (or rock) when being greater than given threshold value, otherwise be rock (or coal), δ is given parameter.

Accompanying drawing explanation

By following explanation, accompanying drawing embodiment becomes aobvious and sees, its only with at least one described by reference to the accompanying drawings preferably but the way of example of non-limiting example provide.

Fig. 1 is the principle schematic of Coal-rock identification method of the present invention.

Specific embodiments

Fig. 1 is the principle schematic of Coal-rock identification method of the present invention, mainly comprises 3 layers: image layer, coding layer and pond layer, and image layer provides input to coding layer, the present embodiment input of abstract image block from gray level image as coding layer; Coding layer calculates expression coefficient when each image block MK primitive is expressed, the present embodiment l ₁-norm optimizes calculation expression coefficient, makes the nonzero element in expression coefficient little, is thus called sparse coding; Pond layer calculates the statistical property of all expression coefficients and then obtains the feature representation of input picture, and concrete implementation step is as follows:

A. gather from scene such as the coal-face of coal and rock identify task the image that M+1 opens coal (or rock), unification zooms to suitable size if 32*32 pixel size is as sample image;

B. from each sample image, the N number of image block of each extraction amounts to (M+1) N number of image block [x ₁, x ₂... x _{(M+1) N}]=X ∈ R ^{p × (M+1) N}, p is the dimension after image block vectorization, as tile size gets 16*16 pixel, is that 4 pixels are slided sampled images block in sample image, carries out standardization: use each image block vector with step-length remove the average of brightness of image, to eliminate the impact of brightness change, use image block vector is normalized, wherein, 1 _prepresent complete 1 vector of p dimension, η is constant value;

C. the image block x that wherein M opens image is got _ias the primitive matrix D=[d of coal (or rock) image ₁, d ₂... d _mN] ∈ R ^{p × MN};

D. by separating optimization problem obtain each image block x of the remaining figure expressed with D _iprimitive coefficient b _i∈ R ^{mN × 1}, image block x _ifeature be and image block x _ithe average of the primitive coefficient of same position is namely: wherein C is normaliztion constant, all image block x _istructural feature matrix U, characteristics of image gets this matrix diagonals element, i.e. F=diag (U); Problem wherein optimizes available approximate gradient algorithm, to each image block x _icorresponding coefficient u _iemploying the following step is optimized:

1. give coefficient u initialize, iterations is set;

2. in iteration each time:

U ← u+ ξ D ^t(x-Du), ξ is iteration step length;

$u\[k\]=\left\{\begin{array}{cc}u\[k\]-\mathrm{\λ}& ifu\[k\]\≥\mathrm{\λ}\\ u\[k\]+\mathrm{\λ}& ifu\[k\]\≤-\mathrm{\λ}\\ 0& otherwise\end{array}\right.,$ λ is given parameter, and k is primitive element index;

3. repeat 2 until iteration terminates.

E. for coal petrography image to be identified, N number of image block y is extracted by the method identical with step B _i, express image block y with the D in step C _i, obtain characteristics of image V to be identified by the method identical with step D;

F. use the similarity of tolerance and known coal or rock object, be coal (or rock) when being greater than given threshold value, otherwise be rock (or coal), δ is given parameter.

Claims (1)

1. a Coal-rock identification method for structure based sparse coding, is characterized in that comprising the following steps:

A. the image that M+1 opens known coal (or rock) object is gathered;

B. N number of image block (M+1) N number of image block [x is altogether extracted from often opening image respectively ₁, x ₂... x _{(M+1) N}]=X ∈ R ^{p × (M+1) N}, p is the dimension after image block vectorization;

C. the image block x that wherein M opens image is got _ias the primitive matrix of coal (or rock) image $D=\[d_1,d_2,...d_{MN}\]\∈R^{p\×MN};$

D. by separating optimization problem obtain each image block x of the remaining figure expressed with D _iprimitive coefficient b _i∈ R ^{mN × 1}, image block x _ifeature be and image block x _ithe average of the primitive coefficient of same position is namely: wherein C is normaliztion constant, all image block x _istructural feature matrix U, characteristics of image gets this matrix diagonals element, i.e. F=diag (U);

E. for coal petrography image to be identified, N number of image block y is extracted by the method identical with step B _i, express image block y with the D in step C _i, obtain characteristics of image V to be identified by the method identical with step D;

F. use the similarity of tolerance and known coal or rock object, be coal (or rock) when being greater than given threshold value, otherwise be rock (or coal), δ is given parameter.