CN105243400A - Coal rock recognition method based on maximum value pooling sparse coding - Google Patents

Abstract

The invention discloses a coal rock recognition method based on maximum value pooling sparse coding. According to the method, structural motifs of coal rocks are learnt in coal rock image data, and the learnt structural motifs capture essential structural features of a coal rock image, so that the resolving ability and the robustness on imaging environment changes are very strong, and as a result, the method has very high recognition stability and recognition correctness, and can provide reliable coal rock recognition information for automatic mining, automatic coal discharge, automatic waste rock selection and other production processes.

Description

Based on the Coal-rock identification method of maximum value pond sparse coding

Technical field

The present invention relates to a kind of Coal-rock identification method based on maximum value pond 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 based on maximum value pond sparse coding, the method is from the structural motif of coal petrography view data learning coal petrography, learn the architectural feature that structural motif captures coal petrography image essence, 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 based on maximum value pond sparse coding is provided, comprises the steps:

A. the image of a known coal and rock object is respectively gathered;

B. from coal petrography image, each extraction is N number of is respectively total to 2N image block [x ₁, x ₂... x _2N]=x ∈ R ^{p × 2N}, p is the dimension after image block vectorization;

C. with the coal extracted and rock image block x _iby separating optimization problem obtain the primitive matrix D=[d of coal and rock image ₁, d ₂... d _k] ∈ R ^{p × K}with the sparse coefficient matrix U=[u of 2N image block ₁, u ₂... u _2N] ∈ R ^{k × 2N}, ui=[u _i1, u _i2... u _iK] ^t;

D. the primitive response distribution z of coal and rock image is obtained respectively _cand z _r, wherein z _corr=[z ₁, z ₂... z _k], a kth primitive response z _k=max{|u _{1, k}|, | u _{2, k}| ..., | u _{n, k}|;

E. for coal petrography image to be identified, extract N number of image block yi by the method identical with step B, express image block y with the D tried to achieve in step C _i, by separating optimization problem obtain the coefficient of each image block $V_i^{*}={\[v_{i1},v_{i2},...v_{iK}\]}^T;$

F. the primitive response distribution of coal petrography image to be identified is obtained by the method identical with step D

G. use the similarity of tolerance and known coal and rock object respectively, is greater than the threshold value of setting when distance and is greater than with the distance of coal and is then judged to be coal with the distance of rock, otherwise being rock;

Further specifically but without limitation, the optimization method of step C is:

C1. give D initialize, iterations is set;

C2. fix D, use $u_i^{*}=\arg {\min }_{u_i}\frac{1}{2}\left|\right|x_i-{\mathrm{Du}}_i|{|}_2^2+\mathrm{\λ}|\left|u_i\right|{|}_1$ Obtain the coefficient U of all image blocks;

C3. fix U, ask $D\∈\arg {\min }_D{\mathrm{\Σ}}_{i=1}^N\frac{1}{2}\left|\right|x_i-{\mathrm{Du}}_i|{|}_2^2;$

C4.C2 and C3 hockets until iteration terminates.

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 comprise 3 layers: image layer, coding layer and pond layer, image layer provides input to coding layer, the present embodiment with abstract image block from gray level image as coding layer input, also can from image abstract image characteristic set if sift feature is as input; Coding layer calculate each image block with from coal petrography image blend data learning to K primitive express time expression coefficient, according to computing method make to express nonzero element in coefficient seldom, be then called sparse coding, the present embodiment l ₁-norm optimizes calculation expression coefficient, and thus the coefficient of gained is sparse; Pond layer calculates the statistical property of all expression coefficients and then obtains the feature representation of input picture, the present embodiment maximum value statistical property, and concrete implementation step is as follows:

A. from the scene of coal and rock identify task as coal-face collection comprises the image of coal and rock, therefrom intercept the image-region only comprising coal and only comprise rock, then unification normalizes to suitable size if 32*32 pixel size is as coal petrography sample image;

B. the N number of image block of each extraction 2N image block [x altogether from two sample images ₁, x ₂... x _2N]=x ∈ R ^{p × 2N}, p is the dimension after image block vectorization; As tile size gets 6*6 pixel, be that 2 pixels are slided sampled images block in sample image with step-length, standardization carried out to each image block vector: use 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. with the coal extracted and rock image block x _iby separating optimization problem obtain the primitive matrix D=[d of coal and rock image ₁, d ₂... d _k] ∈ R ^{p × K}with the sparse coefficient matrix U=[u of 2N image block ₁, u ₂... u _2N] ∈ R ^{k × 2N}, u _i=[u _i1, u _i2... u _iK] ^t;

Due to employing is l ₁-norm optimizes, the u obtained _ivery sparse, namely nonzero element is little.

Method for solving can adopt the method for alternating minimization D and U, namely follows these steps to process:

C1. give D initialize, iterations is set;

C2. fix D, use $u_i^{*}=\arg {\min }_{u_i}\frac{1}{2}\left|\right|x_i-{\mathrm{Du}}_i|{|}_2^2+\mathrm{\λ}|\left|u_i\right|{|}_1$ Obtain the sparse coefficient U of all image blocks;

C3. fix U, ask $D\∈\arg {\min }_D{\mathrm{\Σ}}_{i=1}^N\frac{1}{2}\left|\right|x_i-{\mathrm{Du}}_i|{|}_2^2;$

C4.C2 and C3 hockets until iteration terminates.

The available approximate gradient algorithm of optimization of step C2, 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}& \begin{array}{cc}if& u\[k\]\≥\mathrm{\λ}\end{array}\\ u\[k\]+\mathrm{\λ}& \begin{array}{cc}if& u\[k\]\≤-\mathrm{\λ}\end{array}\\ 0& otherwise\end{array}\right.,$ λ is given parameter, and k is primitive element index;

3. repeat 2 until iteration terminates.

The optimization of step C3 can adopt block coordinate descent algorithm, optimizes with the following step:

1.B←XU ^T，C←UU ^T

2.Fork＝1，2，...，K

$d_k\←\frac{1}{C\[k,k\]}(b_k-{\mathrm{Dc}}_k)+d_k,$

$d_k\←\frac{1}{max\left(\right|\left|d_k\right|{|}_2),1)}{d}_k$

3. repeat 2 until convergence.

D. the primitive response distribution z of coal and rock image is obtained respectively _cand z _r, wherein z _corr=[z ₁, z ₂... z _k], a kth primitive response z _k=max{|u _{1, k}|, | u _{2, k}| ..., | u _{2N, k}|;

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 tried to achieve in step C _i, by separating optimization problem obtain the coefficient of each image block $V_i^{*}={\[v_{i1},v_{i2},...v_{iK}\]}^T;$

F. the primitive response distribution of coal petrography image to be identified is obtained by the method identical with step D

G. use the similarity of tolerance and known coal and rock object respectively, is greater than the threshold value of setting when distance and is greater than with the distance of coal and is then judged to be coal with the distance of rock, otherwise being rock.

Claims (2)

1., based on a Coal-rock identification method for maximum value pond sparse coding, it is characterized in that comprising the following steps:

A. the image of a known coal and rock object is respectively gathered;

B. from coal petrography image, each extraction is N number of is respectively total to 2N image block [x ₁, x ₂... x _2N]=X ∈ R ^{p × 2N}, p is the dimension after image block vectorization;

C. with the coal extracted and rock image block x _iby separating optimization problem obtain the primitive matrix D=[d of coal and rock image ₁, d ₂... d _k] ∈ R ^{p × K}with the sparse coefficient matrix of 2N image block

D. the primitive response distribution z of coal and rock image is obtained respectively _cand z _r, wherein z _corr=[z ₁, z ₂... z _k], a kth primitive response

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 tried to achieve in step C _i, by separating optimization problem obtain the coefficient of each image block

F. the primitive response distribution of coal petrography image to be identified is obtained by the method identical with step D

G. use the similarity of tolerance and known coal and rock object respectively, is greater than the threshold value of setting when distance and is greater than with the distance of coal and is then judged to be coal with the distance of rock, otherwise being rock.

2. method according to claim 1, is characterized in that the optimization method of step C is:

C1. give D initialize, iterations is set;

C2. fix D, use obtain the coefficient U of all image blocks;

C3. fix U, ask

C4.C2 and C3 hockets until iteration terminates.