CN108764313A - Supermarket's commodity recognition method based on deep learning - Google Patents

Supermarket's commodity recognition method based on deep learning Download PDF

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CN108764313A
CN108764313A CN201810475610.5A CN201810475610A CN108764313A CN 108764313 A CN108764313 A CN 108764313A CN 201810475610 A CN201810475610 A CN 201810475610A CN 108764313 A CN108764313 A CN 108764313A
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CN108764313B (en
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董伟生
蒋剑锋
石光明
袁鹏
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Xidian University
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    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
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    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
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Abstract

The present invention proposes a kind of supermarket's commodity target identification method based on deep learning, solves the problems, such as that prior art discrimination in true supermarket's scene is low.Its implementation is：1) supermarket shelves commodity training set is made；2) structure commodity detect network and are trained on the training set of making；3) shelf picture is input to and obtains commodity target area all in picture in trained network model；4) using the output of last in network model layer convolutional layer as commodity clarification of objective；5) product features are encoded to obtain descriptive labelling；6) similarity of descriptive labelling and goods model description in existing model library is calculated；7) using most like goods model as recognition result.The present invention can accurately detect commodity target area in shelf picture, and can correctly identify commodity target, can be used for supermarket shelves merchandise control.

Description

Supermarket's commodity recognition method based on deep learning
Technical field
The invention belongs to technical field of image processing, more particularly to a kind of commodity on shelf target identification method can be applied to Supermarket shelves merchandise control.
Background technology
In supermarket, businessman and consumer need to obtain the merchandise related information on shelf in real time.These quotient at present The relevant information of product is all but the commodity huge amount of supermarket by manually obtaining, the artificial mode for obtaining merchandise news at This height and efficiency is low, therefore the commodity recognition method of view-based access control model has important research significance and commercial value.
The core of supermarket's commodity identification is Target detection and identification, the paper that Shaoqing Ren et al. are delivered at it “Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks”(IEEE Transactions on Pattern Analysis&Machine Intelligence,2015,39 (6):A kind of Target detection and identification method based on deep learning is proposed in 1137-1149).This method passes through region first It generates network and obtains the object candidate area in image；Then the feature vector of convolutional neural networks extraction candidate region is utilized, Region of interest ROI pond is carried out to obtained feature vector, the feature of Chi Huahou is connected with full articulamentum, passes through grader Target is identified；It finally recycles bounding box to return and refine is carried out to the position of target area.This method can be adaptive Ground obtains the high-level semantics feature of target, accurately detects that target area position, accuracy of identification are high.But this method cannot still answer It is identified for supermarket shelves commodity, the reason is that：1, this method carries out region of interest ROI pond to the feature vector of candidate region It can lead to the information loss compared with multiple target when change, when commodity on shelf size is smaller, accuracy of identification will reduce；2, this method is Recognition methods based on statistics, supermarket has new commodity often to be occurred, and old commodity are eliminated, it is therefore desirable to constantly be instructed again Practice grader, cannot meet the needs of reality scene.
Invention content
It is an object of the invention in view of the above shortcomings of the prior art, propose a kind of supermarket's commodity based on deep learning Recognition methods meets the needs of actual scene application to improve the accuracy of detection and recognition effect of supermarket's commodity target.
The technical scheme is that：Acquire supermarket shelves commodity picture, to each pictures mark commodity region and Classify to commodity, is made into training dataset；Structure network model is trained on data set, by learning To the regression function and grader of commodity target area；The commodity region for including in picture is acquired by trained model And classification, Selection Model convolutional layer exports the character representation as end article and quantifies to it, in the same category Object model library in carry out matching realize commodity on shelf target identification.
Implementation step includes as follows：
(1) training sample set is made：
(1a) acquires 3000 shelf pictures for including different commodity in major supermarket by mobile device；
(1b) marks all commodity target windows and classification in shelf picture, commodity target area window upper left by hand Two coordinate representations of angular vertex and bottom right angular vertex, i.e. (x1,y1,x2,y2), according to shape and purposes to commodity target classification；
(1c) upsets shelf picture sequence at random, therefrom chooses 2500 pictures as training sample set, 500 pictures are made For test sample collection；
(2) network model is trained：
(2a) builds commodity and detects network model, using training sample set picture as the input sample of the network, by commodity The classification of target area coordinates and commodity is as output sample；
(2b) utilizes the Area generation network and sorter network in backpropagation BP algorithm alternative optimization (2a) model, more New network parameter obtains trained Area generation network and sorter network；
(3) commodity target area and feature are extracted：
Shelf picture is input in (2b) trained Area generation network by (3a), obtains commodity target area coordinates (x1,y1,x2,y2), and merchandise classification is determined by (2b) trained sorter network；
(3b) will input picture and detect the 13rd convolutional layer output of network model as entire shelf picture in commodity Character representation；
The target area coordinates that (3c) is obtained according to (3a), intercept on (3b) characteristic pattern Region is indicated as commodity clarification of objective；
(4) gauss hybrid models are used to build code book to (3c) commodity clarification of objective, with expectation maximization EM algorithms pair The parameter of gauss hybrid models is estimated, Fisher Vector description of end article are obtained；
(5) match cognization commodity target：
(5a) assumes there is N number of description in object model library, calculates the end article Fisher Vector obtained in (4) Description and the COS distance for being described son in the same category goods model library：
Wherein LiIndicate Fisher Vector descriptions and i-th of goods model library Fisher Vector of end article The COS distance of son is described,It is Fisher Vector description of end article,It is the Fisher in goods model library Vector description, | | x | | it isMould, | | y | | beMould, " " indicate vector inner product.
(5b) sorts (5a) COS distance value calculated from big to small, chooses and the maximum quotient of commodity target COS distance Product model is as recognition result.
Compared with prior art, the present invention haing the following advantages：
1. the present invention is combined deep learning with based on matched recognition methods, when having new commodity appearance, old commodity When eliminating, re -training network model is not needed, it is only necessary to update standard merchandise model library, disclosure satisfy that the need of reality scene It asks.
2. the present invention is using the output of convolutional neural networks last layer as commodity target signature, in commodity object matching When, commodity clarification of objective vector is encoded using Fisher Vector polymerizations, compared with prior art, this hair It is bright to build visual dictionary using gauss hybrid models, the frequency of visual dictionary appearance has not only been counted, visual dictionary has also been counted With the difference of local feature, matched accuracy is improved, and then improve end article recognition accuracy.
Description of the drawings
Fig. 1 is the implementation flow chart of the present invention；
Specific implementation mode
Join Fig. 1, the realization step of the present invention is described in further detail.
It detects step 1. commodity target area.
1.1) 3000 shelf pictures for including different commodity are acquired by mobile device in major supermarket；
1.2) by marking all commodity target windows and classification in shelf picture by hand, i.e., commodity target area is used Window top left corner apex and two coordinate representations of bottom right angular vertex are (x1,y1,x2,y2), further according to commodity shape and purposes into Commodity are divided into 31 classes by row target classification, this example：It is sundries tool, bottled cleaning supplies, bottled drink, bottled condiment, bottled Wine, bottled washing product, bottled snacks, packed seasoning, packed snacks, packed food materials, packed paper handkerchief, bagged clean articles for use, bag Fill daily necessities, canned can, tinned drink, canned milk powder, canned wine, box-packed snacks, box-packed toy, box-packed drink, box-packed day Articles for use, box-packed washing product, box-packed food materials, cup, bowl, packet, chest, basin, bucket, cup article, oil；
1.3) shelf picture is named according to " 000001.jpg " format, the name of 3000 shelf pictures is { 000001.jpg, 000002.jpg ... 00i.jpg ..., 003000.jpg }, wherein 00i.jpg is the name of i-th shelf picture Word, 1≤i≤3000 randomly select 2500 pictures as training sample set, 500 pictures in 3000 from this in shelf picture As test sample collection.
1.4) structure commodity detect network model：
If commodity detection network shares 23 layers, it is from bottom to top：Input layer → the first convolutional layer → the second convolution Layer → first pond layer → third convolutional layer → four convolutional layer → the second pond layer → five convolutional layer → the Six convolutional layer → seven convolutional layers → third pond layer → eight convolutional layer → nine convolutional layer → ten volume On convolutional layer → the first of lamination → four pond layer → 11st convolutional layer → 12nd convolutional layer → 13rd It is complete that sample level → Area generation network class returns full articulamentum → the second of layer → region of interest ROI pond layer → the first Articulamentum → classification returns layer；
1.5 utilize the Area generation network and classification net in backpropagation BP algorithm alternative optimization commodity detection model Network, more new commodity detection model parameter obtain trained Area generation network and sorter network, and steps are as follows：
(1.5a) detects network model initialization area with commodity and generates network, using shelf picture as the input of network, Using the commodity coordinates of targets of mark as the output of network, training Area generation network, more new commodity detection network model；
(1.5b) initializes sorter network with (1.5a) newer commodity detection network model, utilizes trained Area generation Network obtains the commodity target area in shelf picture, using obtained commodity target area and shelf picture as sorter network Input, using the merchandise classification of mark as the output of network, training sorter network, more new commodity detects network model；
(1.5c) generates network, second of training center with (1.5b) trained commodity detection network model initialization area Domain generates network, and keeps commodity detection network model constant, and update area generates network parameter；
(1.5d) keeps the commodity detection network model of (1.5c) constant, initializes sorter network, second of training classification Network updates the parameter of sorter network, finally obtains trained Area generation network and sorter network；
1.6) shelf picture is input in trained Area generation network, obtains commodity target area, the target area Domain is by upper left point coordinates (x1,y1) and bottom right point coordinates (x2,y2) indicate, i.e., the commodity target area is expressed as (x1,y1,x2, y2), and obtained commodity target area and shelf picture are input to trained sorter network, obtain merchandise classification.
Step 2. commodity target's feature-extraction.
2.1) the 13rd convolutional layer output that shelf picture is detected to network of network model in commodity is used as entire shelf The character representation of picture；
2.2) the commodity target area coordinates (x obtained according to step 11,y1,x2,y2), it is cut on commodity on shelf characteristic pattern It takesRegion is indicated as commodity clarification of objective.
Step 3. encodes commodity clarification of objective.
3.1) gauss hybrid models are used to build code book to step 2 commodity clarification of objective：
(3.1a) assumes that the feature point number of commodity Objective extraction is T, is by this commodity object representation：
X={ xt, t=1....T }, wherein xtIt is feature vector；
(3.1b) assumes feature xtIt is independent identically distributed, then gauss hybrid models are by xtIt is expressed as：
Wherein M is the number of Gaussian component, ω={ ωk, k=1 ... M }, μ={ μk, k=1 ... M }, ∑={ ∑k,k =1 ... M }, ωkIt is the weights of k-th of Gaussian component, N (xt|μk,∑k) it is k-th of Gaussian Profile in mixed model point Amount, μkIt is the expectation of k-th of Gaussian component, ∑kIt is the variance of k-th of Gaussian component, k-th of Gaussian Profile component formula is：Wherein exp is using natural constant e as the finger at bottom Number function, T indicate transposition operation, ∑k -1It is variance matrix ∑kInverse matrix；
3.2) parameter of gauss hybrid models is estimated with expectation maximization EM algorithms, obtains end article Fisher Vector description：
The weights ω of (3.2a) to each Gaussian componentk, it is expected that μkWith variance ∑kInitial value is set；
(3.2b), which is introduced, implies variable znk, according to znkValue judges n-th of feature vector xnWhether k-th Gauss point is belonged to Amount：If znk=1, then it represents that n-th of feature vector xnBelong to k-th of Gaussian component, if znk=0, then it represents that n-th of feature Vector xnIt is not belonging to k-th of Gaussian component；
(3.2c) works as znkWhen=1, according to current ωk, μkAnd ∑kCalculate xnPosterior probability γ (znk)：
(3.2d) is according to the γ (z calculated in (3.2c)nk) undated parameter ωk, μkAnd ∑k
Wherein：
(3.2e) is according to (3.2d) updated parameter ωk, μkAnd ∑k, calculate the log-likelihood letter of feature vector set Number：
Wherein X={ xn, n=1....T } be commodity Objective extraction T feature vector set, ω={ ωk, k=1, ... M }, μ={ μk, k=1 ... M }, ∑={ ∑k, k=1 ... M }；
(3.2f) judges whether (3.2e) likelihood function restrains, will more if convergence if not restraining return (3.2c) ω after newk, μkAnd ∑kValue as gauss hybrid models parameter value；
The feature vector set X={ x of (3.2g) to expression picturen, n=1....T } and calculate log-likelihood function：Wherein p (xn| ω, μ, ∑) it is the gauss hybrid models that (3.1b) is built, ω ={ ωk, k=1 ... M }, μ={ μk, k=1 ... M }, ∑={ ∑k, k=1 ... M }, ωkIt is the power of k-th of Gaussian component Value, μkIt is the expectation of k-th of Gaussian component, ∑kIt is the variance of k-th of Gaussian component；
(3.2h) is to ω in (3.2g) log-likelihood function L (X | ω, μ, ∑)k, μk, ∑kSeek local derviation：
Wherein ωk, μk, ∑kIt is the parameter that (3.2f) is acquired, it is assumed that feature vector xnDimension be D,Indicate feature to Measure xnD dimension,It indicates it is expected μkD dimension,Indicate variance ∑kD dimension, 1≤d≤D, γ (znk) it is (3.2c) meter The feature x of calculationnPosterior probability；
(3.2i) is by three local derviations in (3.2h)After normalization, combination Gather at a vector, vector set is exactly feature vector set X={ xn, n=1....T } Fisher Vector description Son.
Step 4. match cognization commodity target.
Assuming that there is N number of description in object model library, calculates the end article Fisher Vector obtained in step 3 and retouch State son and the COS distance for being described son in the same category object model library：Wherein：
LiFisher Vector descriptions and i-th of object model library Fisher Vector for indicating end article describe The COS distance of son,It is Fisher Vector description of end article,It is the Fisher Vector in physical model library Description, | | x | | it isMould, | | y | | beMould, " " indicate vector inner product；
The COS distance value being calculated is sorted from big to small, chooses and makees with the maximum object of commodity target COS distance For matching result, commodity target area is identified.
1. simulated conditions：
The emulation experiment of the present invention be the GPU of monolithic NVIDIA GTX 1080Ti models, running memory 128GB it is hard It is carried out under the software environment of part environment and Caffe.
2. emulation content and interpretation of result：
Before emulation experiment, a model library for possessing 3975 commodity is first built；
Selected 30 shelf pictures for including 305 commodity targets in total；
It is tested using 30 shelf pictures of this method pair, detects 305 commodity target areas, while obtaining this Description of 305 commodity targets will obtain each describing son and the goods model description in the goods model library of structure is sub It compares, finds out most like goods model as recognition result, the commodity target that this experiment correctly identifies has 275, identification Accuracy rate be 0.9016.
The experimental results showed that in supermarket's reality scene, the preferable identification to commodity target can be realized with the present invention.

Claims (5)

1. a kind of supermarket's commodity recognition method based on deep learning, includes the following steps：
(1) training sample set is made：
(1a) acquires 3000 shelf pictures for including different commodity in major supermarket by mobile device；
(1b) marks all commodity target windows and classification in shelf picture by hand, and commodity target area is pushed up with the window upper left corner Two coordinate representations of point and bottom right angular vertex, i.e. (x1,y1,x2,y2), according to shape and purposes to commodity target classification；
(1c) upsets shelf picture sequence at random, therefrom chooses 2500 pictures as training sample set, 500 pictures are as survey Try sample set；
(2) network model is trained：
(2a) builds commodity and detects network model, using training sample set picture as the input sample of network, by commodity target area The classification of domain coordinate and commodity is as output sample；
(2b) utilizes the Area generation network and sorter network in backpropagation BP algorithm alternative optimization (2a) model, updates net Network parameter obtains trained Area generation network and sorter network；
(3) commodity target area and feature are extracted：
Shelf picture is input in (2b) trained Area generation network by (3a), obtains commodity target area coordinates (x1,y1, x2,y2), and merchandise classification is determined by (2b) trained sorter network；
(3b) will input picture and detect spy of the 13rd convolutional layer output of network model as entire shelf picture in commodity Sign indicates；
The target area coordinates that (3c) is obtained according to (3a), intercept on (3b) characteristic patternArea Domain is indicated as commodity clarification of objective；
(4) gauss hybrid models are used to build code book to (3c) commodity clarification of objective, with expectation maximization EM algorithms to Gauss The parameter of mixed model is estimated, Fisher Vector description of end article are obtained；
(5) match cognization commodity target：
(5a) assumes there is N number of description in object model library, calculates the end article Fisher Vector descriptions obtained in (4) Son and the COS distance for being described son in the same category goods model library：
Wherein LiFisher Vector descriptions and i-th of goods model library Fisher Vector for indicating end article describe The COS distance of son,It is Fisher Vector description of end article,It is the Fisher Vector in goods model library Description, | | x | | it isMould, | | y | | beMould, " " indicate vector inner product.
(5b) sorts (5a) COS distance value calculated from big to small, chooses and the maximum commodity mould of commodity target COS distance Type is as recognition result.
2. according to the method described in claim 1, the commodity wherein in step (2a) detect network model, structure is from bottom to top For：Input layer → the first convolutional layer → the second convolutional layer → the first pond layer → third convolutional layer → four convolution Layer → second pond layer → five convolutional layer → six convolutional layer → seven convolutional layer → third pond layer → the Eight convolutional layer → nine convolutional layer → ten convolutional layer → four ponds layer → 11st convolutional layer → 12nd A convolutional layer → 13rd convolutional layer → the first up-sampling layer → Area generation network class returns layer → area-of-interest Full articulamentum → the classification of full articulamentum → the second of the ponds ROI layer → the first returns layer.
3. according to the method described in claim 1, wherein utilizing backpropagation BP algorithm alternative optimization (2a) mould in step (2b) Area generation network in type and sorter network update network parameter, carry out as follows：
(2b1) detects network model initialization area generation network with commodity and will be marked using shelf picture as the input of network Output of the commodity coordinates of targets as network, training Area generation network, more new commodity detects network model；
(2b2) initializes sorter network with (2b1) newer commodity detection network model, is obtained using trained Area generation network To the commodity target area in shelf picture, using obtained commodity target area and shelf picture as the defeated of sorter network Enter, using the merchandise classification of mark as the output of network, training sorter network, more new commodity detection network model；
(2b3) generates network, second of training Area generation net with the trained commodity detection network model initialization domain (2b2) Network, and keep commodity detection network model constant, update area generates network parameter；
(2b4) keeps the commodity detection network model of (2b3) constant, initializes sorter network, for the second time training sorter network, more The parameter of new sorter network, finally obtains trained Area generation network and sorter network.
4. according to the method described in claim 1, using gauss hybrid models to commodity clarification of objective structure wherein in step (4) Code book is built, is carried out as follows：
(4a) assumes that the feature point number of commodity Objective extraction is T, is by this commodity object representation：X={ xt, t= 1....T }, wherein xtIt is feature vector；
(4b) assumes feature xtIt is independent identically distributed, then gauss hybrid models are by xtIt is expressed as：
Wherein M is the number of Gaussian component, ω={ ωk, k=1 ... M }, μ={ μk, k=1 ... M }, ∑={ ∑k, k= 1 ... M }, ωkIt is the weights of k-th of Gaussian component, N (xt|μk,∑k) it is k-th of Gaussian Profile component in mixed model, μk It is the expectation of k-th of Gaussian component, ∑kIt is the variance of k-th of Gaussian component, k-th of Gaussian Profile component formula is：
5. according to the method described in claim 1, with expectation maximization EM algorithms to gauss hybrid models wherein in step (4) Parameter is estimated, carries out as follows：
The weights ω of (4c) to each Gaussian componentk, it is expected that μkWith variance ∑kSetting initial value；
(4d), which is introduced, implies variable znk, according to znkValue judges n-th of feature vector xnWhether k-th Gaussian component is belonged to：If znk=1, then it represents that n-th of feature vector xnBelong to k-th of Gaussian component, if znk=0, then it represents that n-th of feature vector xn It is not belonging to k-th of Gaussian component；
(4e) works as znkWhen=1, according to current ωk, μkAnd ∑kCalculate xnPosterior probability γ (znk)：
(4f) is according to the γ (z calculated in (4e)nk) undated parameter ωk, μkAnd ∑k
Wherein：
(4g) is according to (4f) updated parameter ωk, μkAnd ∑k, calculate the log-likelihood function of feature vector set：
Wherein X={ xn, n=1....T } be commodity Objective extraction T feature vector set, ω={ ωk, k=1 ... M }, μ ={ μk, k=1 ... M }, ∑={ ∑k, k=1 ... M }.
(4h) judges whether (4g) likelihood function restrains, if not restraining return (4e), by updated ω if convergencek, μk And ∑kValue as model parameter value.
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