CN110473130A - A kind of garbage classification evaluation method, device and storage medium based on deep learning - Google Patents
A kind of garbage classification evaluation method, device and storage medium based on deep learning Download PDFInfo
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Abstract
The invention discloses a kind of garbage classification evaluation method, device, equipment and storage medium based on deep learning.Obtain the rubbish image launched；The rubbish image that will acquire inputs and carries out identification classification into having been subjected to trained convolutional neural networks model；It will identify that sorted result carries out scoring calculating, and the part for having launched wrong rubbish on image will be marked；Appraisal result after calculating and the image being labeled are exported to display module.It is scored by working as time image of dispensing to rubbish, and the part of corresponding mistake is marked, image after appraisal result and label is shown on display module again, rubbish dispensing user can be given in time to timely feedback when the whether correct information of subseries garbage throwing, the part that rubbish launches mistake can be understood at the first time by allowing rubbish to launch user, can targetedly supplement relevant garbage classification knowledge.
Description
Technical field
The present invention relates to Waste sorting recycle field, in particular to a kind of garbage classification evaluation side based on deep learning
Method, device, equipment and storage medium.
Background technique
Currently, the requirement for putting on by classification rubbish to people is also being continuously improved, for the sustainable development of environment in order to mention
The efficiency of high administrative department's garbage reclamation and processing, the classification that rubbish is divided into are also increasingly thinner.Under normal circumstances, people are to dispensing
Rubbish first classify by the classification standard of relevant departments, in the garbage recovery device for then launching respective classes again.But
It is people when garbage throwing, is not that can be put on by classification well according to the standard of garbage classification every time, is
Solution such problems, it will usually artificial consultant be set in the place of garbage reclamation, be responsible for checking the rubbish that people launch
Whether correct classify, and correction guidance is carried out to the behavior of mistake classification.But such method inefficiency, in the same time
When needing to carry out garbage classification dispensing if there is more people, people's many times can be expended.In the prior art, also there is proposition
The garbage reclamation scheme of intelligence first first carries out certain authentication to the individual of garbage throwing, then passes through image recognition
Mode carries out Classification and Identification to the rubbish launched into garbage retrieving system, then is what backstage was recorded in the result information of identification
System, it is convenient that subsequent supervision correction is carried out to the behavior that garbage classification is launched.Although this scheme can improve people and put on by classification
The efficiency of rubbish, but it is unable to the case where visual feedback people's single puts on by classification rubbish, it cannot be in time to of garbage throwing
People correctly instructs.
Summary of the invention
It is an object of the invention at least solve one of the technical problems existing in the prior art, provide a kind of based on depth
Garbage classification evaluation method, device, equipment and the storage medium of study, can visual feedback people's single put on by classification rubbish
Situation.
The first aspect of the present invention provides a kind of garbage classification evaluation method based on deep learning, comprising the following steps:
Obtain the rubbish image launched；
The rubbish image that will acquire inputs and carries out identification classification into having been subjected to trained convolutional neural networks model；
It will identify that sorted result carries out scoring calculating, and the part for having launched wrong rubbish on image will be marked
Note；
Appraisal result after calculating and the image being labeled are exported to display module.
The above-mentioned garbage classification evaluation method based on deep learning at least has the advantages that by working as to rubbish
The image of secondary dispensing scores, and the part of corresponding mistake is marked, and the image after appraisal result and label is shown again
Show and shown in module, rubbish dispensing user can be given in time and timely feedbacked when the whether correct information of subseries garbage throwing,
The part that rubbish launches mistake can be understood at the first time by allowing rubbish to launch user, can targetedly supplement relevant rubbish
Classificating knowledge.
The garbage classification evaluation method based on deep learning according to a first aspect of the present invention, it is described that identification is classified
Result afterwards carries out the specific execution of scoring calculating or less and calculates:
Wherein S is the appraisal result of output, and i is the serial number of rubbish classification, xiFor the quantity of the i-th class rubbish identification, piIt is
The classification accuracy of i class rubbish, α are preset correction factor, and the appraisal result S is exported to display module.Pass through above-mentioned meter
Calculation mode can directly export a fractional value to display module, and a direct fractional value can more intuitively show and work as
The correct degree that secondary garbage classification is launched.
The garbage classification evaluation method based on deep learning according to a first aspect of the present invention,
If exporting the appraisal result is to fail, corresponding rubbish image is stored to artificial screening atlas；
If the image of the artificial screening atlas is marked as identifying wrong picture, the identification error image is stored
To the training atlas of the convolutional neural networks model, retraining is carried out.
Backstage is recorded in the appraisal result.It is inputted in convolutional neural networks model by the image to system identification mistake
Retraining is carried out, the accuracy rate of identification model is further promoted, while recording appraisal result on backstage, facilitates rubbish recycling management
Department formulates Managed Solution based on the big data of appraisal result.
The garbage classification evaluation method based on deep learning according to a first aspect of the present invention further includes launching rubbish
The identity information that rubbish launches user is obtained by recognition of face mode before rubbish.By the method for recognition of face, obtain when time rubbish
Rubbish puts on by classification the identity information of user, the behavior that relative users and the secondary garbage classification are launched can be associated record,
Targeted educational guidance is carried out to corresponding user convenient for rubbish recycling management department.
The garbage classification evaluation method based on deep learning according to a first aspect of the present invention, the convolutional Neural net
Network model includes multiple convolutional layers, and the first convolutional layer of the multiple convolutional layer is the mark with batchnorm (BN) and ReLU
Quasi- convolution, the multiple convolutional layer are the separable volume of the depth with batchnorm (BN) and ReLU from second convolutional layer
Lamination, it includes depth convolution sublayer and point-by-point convolution sublayer, the multiple convolutional layer, from second that the depth, which separates convolution,
Convolutional layer starts, two convolutional layers of every increase, and convolution kernel size increases primary.The design of such convolutional Neural model, energy
Enough accuracy of identification that convolutional neural networks are promoted in increase convolution layer number, while rationally control parameter amount, keep convolutional Neural
The normal operation of network model.
The second aspect of the present invention, provides a kind of garbage classification evaluating apparatus based on deep learning, image acquisition unit,
For obtaining the rubbish image launched；
Image identification unit, the rubbish image for will acquire input into have been subjected to trained convolutional neural networks model into
Row identification classification；
Score computing unit, for that will identify that sorted result carries out scoring calculating, and mistake will have been launched on image
The part of rubbish is marked；
Display unit, for showing the appraisal result after calculating and labeled image.
The above-mentioned garbage classification evaluating apparatus based on deep learning at least has the advantages that by working as to rubbish
The image of secondary dispensing scores, and the part of corresponding mistake is marked, and the image after appraisal result and label is shown again
Show and shown in module, rubbish dispensing user can be given in time and timely feedbacked when the whether correct information of subseries garbage throwing,
The part that rubbish launches mistake can be understood at the first time by allowing rubbish to launch user, can targetedly supplement relevant rubbish
Classificating knowledge.
The garbage classification evaluating apparatus based on deep learning according to a second aspect of the present invention, the scoring calculate single
First specific execution is following to be calculated:
Wherein S is the appraisal result of output, and i is the serial number of rubbish classification, xiFor the quantity of the i-th class rubbish identification, piIt is
The classification accuracy of i class rubbish, α are preset correction factor, and the appraisal result S is exported to display module.Pass through above-mentioned meter
Calculation mode can directly export a fractional value to display module, and a direct fractional value can more intuitively show and work as
The correct degree that secondary garbage classification is launched.
The garbage classification evaluating apparatus based on deep learning according to a second aspect of the present invention, further includes recognition of face
Unit, the identity information for launching user for obtaining rubbish before garbage throwing.By the method for recognition of face, obtain when time rubbish
Rubbish puts on by classification the identity information of user, the behavior that relative users and the secondary garbage classification are launched can be associated record,
Targeted educational guidance is carried out to corresponding user convenient for rubbish recycling management department.
The third aspect of the present invention provides a kind of garbage classification valuator device based on deep learning, including at least one
Control processor and memory for being communicated to connect at least one described control processor: the memory is stored with can
The instruction executed by least one described control processor, described instruction are executed by least one described control processor, so that
At least one described control processor is able to carry out the above-mentioned garbage classification evaluation method based on deep learning.
The fourth aspect of the present invention, provides a kind of computer readable storage medium, and the computer readable storage medium is deposited
Contain computer executable instructions, it is above-mentioned based on deep learning that the computer executable instructions are used to making computer to execute
Garbage classification evaluation method.
Detailed description of the invention
The present invention is further explained with reference to the accompanying drawings and examples.
Fig. 1 is a kind of flow chart for garbage classification evaluation method based on deep learning that the embodiment of the present invention one provides；
Fig. 2 is a kind of flow chart of the garbage classification evaluation method based on deep learning provided by Embodiment 2 of the present invention；
Fig. 3 is a kind of flow chart for garbage classification evaluation method based on deep learning that the embodiment of the present invention three provides；
Fig. 4 is a kind of schematic diagram of convolutional neural networks model provided in an embodiment of the present invention
Fig. 5 is a kind of schematic diagram of the garbage classification evaluating apparatus based on deep learning provided in an embodiment of the present invention；
Fig. 6 is a kind of schematic diagram of the garbage classification valuator device based on deep learning provided by the invention.
Specific embodiment
This part will be described in specific embodiments of the present invention, and the preferred embodiments of the invention is shown in the accompanying drawings, attached
The effect of figure be with figure remark additionally book word segment description, enable a person to intuitively, visually understand of the invention
Each technical characteristic and overall technical architecture, but it should not be understood as limiting the scope of the invention.
In the description of the present invention, it is to be understood that, be related to orientation description, for example, above and below, front, rear, left and right etc.
The orientation or positional relationship of instruction is to be based on the orientation or positional relationship shown in the drawings, and is merely for convenience of the description present invention and letter
Change description, rather than the device or element of indication or suggestion meaning must have a particular orientation, with specific orientation construct and
Operation, therefore be not considered as limiting the invention.
In the description of the present invention, several to be meant that one or more, it is multiple to be meant that two or more, be greater than,
Be less than, more than etc. be interpreted as not including this number, it is above, following, within etc. be interpreted as including this number.If there is being described to first,
Second is only intended to for the purpose of distinguishing technical characteristic, is not understood to indicate or imply relative importance or implicitly indicates institute
The quantity of the technical characteristic of instruction or the precedence relationship for implicitly indicating indicated technical characteristic.
In description of the invention, unless otherwise restricted clearly, the words such as setting, installation, connection be shall be understood in a broad sense, institute
Above-mentioned word in the present invention specific can rationally be determined with the particular content of combination technology scheme by belonging to technical field technical staff
Meaning.
Currently, the Waste sorting recycles in many places be all through the way of manual viewing, instruct rubbish launch user by
The classification method of sighting target standard carries out rubbish dispensing.As image recognition technology develops, some intelligent garbage retrieving systems pass through
The mode that camera is arranged obtains rubbish image, and carries out identification classification to the rubbish launched by image recognition algorithm, but
It is that this intelligent garbage recovery system has only carried out corresponding Classification and Identification to the rubbish of dispensing, cannot timely feedbacks when secondary
The correctness of rubbish is put on by classification, rubbish is launched user and cannot timely be found when whether time classification of dispensing is correct.
Based on this, a kind of garbage classification evaluation method, device, equipment and storage medium based on deep learning of the invention,
The rubbish image launched is obtained, the rubbish image that will acquire is inputted and identified into having been subjected to trained convolutional neural networks model
Classification, will identify that sorted result carries out scoring calculating, and the part that wrong rubbish has been launched on image is marked, will
Appraisal result and labeled image after calculating are exported to display module.It is commented by working as time image of dispensing to rubbish
Point, and the part of corresponding mistake is marked, the image appraisal result and after marking is shown on display module again, Neng Gouji
When give rubbish and launch user and timely feedback when the whether correct information of subseries garbage throwing, allowing rubbish to launch user can the
One time understood the part that rubbish launches mistake, can targetedly supplement relevant garbage classification knowledge.
In order to make day of the invention, technical solution and advantage be more clearly understood, it is with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention.
It should be noted that each feature in the embodiment of the present invention can be combined with each other, in this hair if do not conflicted
Within bright protection scope.In addition, though having carried out functional module division in schematic device, shows patrol in flow charts
Sequence is collected, but in some cases, it can be shown in the sequence execution in the module division being different from device or flow chart
The step of out or describing.
Referring to Fig.1, a kind of garbage classification evaluation method based on deep learning that the embodiment of the present invention one provides, including with
Lower step:
S10: the rubbish image of dispensing is obtained；
S20: the rubbish image that will acquire inputs and carries out identification classification into having been subjected to trained convolutional neural networks model；
S30: it will identify that sorted result carries out scoring calculating, and the part for having launched wrong rubbish on image will be carried out
Label；
S40: the appraisal result after calculating and the image being labeled are exported to display module.
Wherein, in the present embodiment, the image for obtaining rubbish mainly uses the picture pick-up device being preset on garbage recovery device,
Optionally, CCD camera can be used, detects whether that there are rubbish in the realtime graphic of camera acquisition, and will be where rubbish
Region is intercepted.Using a kind of two novel step detector Light-Head R-CNN, it is a kind of lightweight inspection for the part
Survey efficient, high-accuracy a two step detectors of device head design construction.Specifically, which uses a big kernel
Revoluble product and a small amount of channel generate sparse characteristic pattern.The calculation amount of this design keeps subsequent RoI sub-network computationally intensive
Width reduces, and memory needed for detection system is reduced.Also a common full articulamentum is attached in the network structure of the detector
On the layer of pond, the character representation of classification and recurrence is taken full advantage of in this way.And convolutional neural networks are by a large amount of weight parameter structures
At reliable weight parameter is the continuous training by mass data, finally reaches what convergence state was obtained, in the present embodiment
In, convolutional neural networks model has been trained by a large amount of corresponding rubbish pictures using preceding.In this example, rubbish
Classification be largely divided into 4 classes, be wet refuse, dry rubbish, recyclable rubbish, Harmful Waste respectively.On display module be default
Display device on garbage recovery device, it is alternatively possible to be LCD display, OLED display.According to rubbish image recognition
As a result, selecting corresponding parameter calculate and marking the price the part of rubbish wrong in image, optionally, corresponding
The practical red circle of image be marked, the result of calculating and labeled image are output in display device, timely feedback to
Rubbish launches the correctness that user launches when time garbage classification, can understand the part that rubbish launches mistake at the first time, can be with
Targetedly supplement relevant garbage classification knowledge.
Referring to Fig. 4, the embodiment of the invention also provides a kind of convolutional neural networks model applied in garbage classification identification,
The model is successively input layer, convolutional layer 1, convolutional layer 2, convolutional layer 3, convolutional layer 4, convolutional layer 5, convolutional layer 6, volume in sequence
Lamination 7, pond layer, Dropout layers, full articulamentum, output layer.Wherein, convolutional layer 1 is with batchnorm (BN) and ReLU
Standard convolution, convolutional layer 2-7 is the separable convolution of the depth with batchnorm (BN) and ReLU, and depth separates convolution
Include two conventional parts, before this depth convolution, followed by point-by-point convolution.Such design can effectively reduce calculating ginseng
Number, reduces the complexity of network model, enhances the robustness of system, improves training effectiveness, can mention increasing convolution layer number
The accuracy of identification of convolutional neural networks is risen, while rationally control parameter amount, keeps the normal operation of convolutional neural networks model.
Specifically, the input picture of convolutional neural networks model is dimensioned to 200*200.
Convolutional layer 1, convolution kernel are dimensioned to 5*5, and step-length 2, the number of convolution kernel is 32, and edge is without filling.Through
64 characteristic patterns, size 99*99 are exported after crossing convolutional layer 1.
Convolutional layer 2, depth convolution kernel are dimensioned to 3*3, step-length 2, and the number of convolution kernel is 32, and edge is without filling out
It fills, point-by-point convolution kernel is dimensioned to 1*1, and step-length 1, the number of convolution kernel is 64, and edge is without filling.By convolutional layer 2
64 characteristic patterns, size 49*49 are exported afterwards.
Convolutional layer 3, depth convolution kernel are dimensioned to 3*3, and the number of step-length 1, convolution kernel is 64, and edge filling is
1, point-by-point convolution kernel is dimensioned to 1*1, and step-length is set as 1, and the number of convolution kernel is 128, and edge is without filling.By convolution
128 characteristic patterns, size 49*49 are exported after layer 3.
Convolutional layer 4, depth convolution kernel are dimensioned to 5*5, step-length 2, and the number of convolution kernel is 128, and edge is without filling out
It fills, point-by-point convolution kernel is dimensioned to 1*1, and step-length is set as 1, and the number of convolution kernel is 256, and edge is without filling.Through pulleying
256 characteristic patterns, size 23*23 are exported after lamination 4.
Convolutional layer 5, depth convolution kernel are dimensioned to 5*5, and step-length 1, the number of convolution kernel is 256, edge filling
It is 1, point-by-point convolution kernel is dimensioned to 1*1, and step-length is set as 1, and the number of convolution kernel is 256, and edge is without filling.Through pulleying
256 characteristic patterns, size 21*21 are exported after lamination 5.
Convolutional layer 6, depth convolution kernel are dimensioned to 7*7, step-length 2, and the number of convolution kernel is 256, and edge is without filling out
It fills, point-by-point convolution kernel is dimensioned to 1*1, and step-length is set as 1, and the number of convolution kernel is 512, and edge is without filling.Through pulleying
512 characteristic patterns, size 8*8 are exported after lamination 6.
Convolutional layer 7, depth convolution kernel are dimensioned to 7*7, and step-length 1, the number of convolution kernel is 512, edge filling
It is 1, point-by-point convolution kernel is dimensioned to 1*1, and step-length is set as 1, and the number of convolution kernel is 1024, and edge is without filling.By
1024 characteristic patterns, size 4*4 are exported after convolutional layer 7.
Pond layer carries out maximum pondization processing to the characteristic pattern that convolutional layer 7 obtains, and the window size of down-sampling is set as 4,
Step-length is set as 2.1024 characteristic patterns, size 1*1 are exported after the layer of pond.
Dropout layers: in order to make network have better adaptability, and for preventing over-fitting.
Full articulamentum, using 1024 neurons to by pond layer down-sampling, treated that characteristic pattern is connected entirely,
Characteristic pattern is converted to one-dimensional feature vector, and result is input to Softmax classifier, which the rubbish for exporting detection belongs to
The classification results of a kind of rubbish.The rubbish set in the present embodiment has four classes.
Before training, the first step first carries out mark to four class rubbish of setting.Rubbish classification uses a1,a2,a3,a4Generation
Table.(1,0,0,0), (0,1,0,0), (0,0,1,0), (0,0,0,1) four matrix distinguish table in training convolutional neural networks
Show a1,a2,a3,a4The output result of this 4 class garbage classification.The data set of training pattern includes the four class rubbish that manual sort completes
Rubbish classification image.In the training process, every to input a training image artificially marked to network model, it can all export the image
Include that a kind of probability.It is trained and multiple by a large amount of data according to the penalty values of the output probability computation model of model
Iteration constantly updates network weight parameter, so that penalty values constantly reduce, until being less than the acceptable critical value being manually set,
Network reaches convergence state, and training stops.The initialization of model is completed.In practical application, only needing collected rubbish
Image is input to the model of training completion after treatment, so that it may obtain the output result of collected rubbish type
Further, above-mentioned to identify that sorted result carries out scoring and calculate specific to execute following calculate:
Wherein S is the appraisal result of output, and i is the serial number of rubbish classification, xiFor the quantity of the i-th class rubbish identification, piIt is
The classification accuracy of i class rubbish, α are preset correction factor, and the appraisal result S is exported to display module.Normally, one
Rubbish picture material includes hurrock, and convolutional neural networks model can detect positioning garbage areas, in this rubbish of positioning
Region convolutional neural networks model can carry out identification classification according to four classifications for presetting classification, but same class rubbish can
Can the region class of this positioning be not connection (for example the upper left corner is Harmful Waste, and it is Harmful Waste that, which there is one piece of region in centre,
The lower right corner is recyclable rubbish), classify then convolutional neural networks model can position this three pieces of regions, then in this knowledge
The result that do not classify is exactly that the quantity of Harmful Waste is 2, and the quantity of recyclable rubbish is 1.It, can be with by above-mentioned calculation
A fractional value is directly exported to display module, a direct fractional value can more intuitively be shown when time garbage classification is thrown
The correct degree put.
Referring to Fig. 2, the embodiment of the present invention two additionally provides a kind of garbage classification evaluation method based on deep learning, wraps
Include following steps:
S210: the identity information that rubbish launches user is obtained by recognition of face mode；
S220: the rubbish image of dispensing is obtained；
S230: the rubbish image that will acquire inputs and carries out identification classification into having been subjected to trained convolutional neural networks model；
S240: will identify that sorted result carries out scoring calculating, and will have been launched on image the part of wrong rubbish into
Line flag；
S250: the appraisal result after calculating and the image being labeled are exported to display module.
The present embodiment mainly increases step S210 compared to embodiment one: obtaining rubbish by recognition of face mode and throws
Put the identity information of user.In the present embodiment, recognition of face moieties option uses FaceNet, it can be used for face and tests
Card, identification and cluster.Different using classification layer from other human face recognition models based on convolutional neural networks, FaceNet is then
Picture is mapped in Euclidean space by convolutional neural networks, space length is related to the similarity of picture, same
The picture of people is in Euclidean space apart from very little, and the distance of the image of different people is then bigger, and recognition of face passes through calculating
Euclidean space distance is completed.By the method for recognition of face, obtain when time identity information of garbage classification dispensing user,
The behavior that relative users and the secondary garbage classification are launched can be associated record, convenient for rubbish recycling management department to corresponding
User carry out targeted educational guidance.
Referring to Fig. 3, the embodiment of the present invention three additionally provides a kind of garbage classification evaluation method based on deep learning, wraps
Include following steps:
S410: the identity information that rubbish launches user is obtained by recognition of face mode；
S420: the rubbish image of dispensing is obtained；
S430: the rubbish image that will acquire inputs and carries out identification classification into having been subjected to trained convolutional neural networks model；
S440: will identify that sorted result carries out scoring calculating, and will have been launched on image the part of wrong rubbish into
Line flag；
S450: the appraisal result after calculating and the image being labeled are exported to display module.
S460: it identifies that the picture of mistake is stored to the training atlas of the convolutional neural networks model, carries out retraining.
Compared to embodiment two, the main distinction is to increase S460 the present embodiment: identifying that the picture of mistake is stored to institute
The training atlas of convolutional neural networks model is stated, retraining is carried out.Above-mentioned steps are specially in appraisal result S < 0.9 for not
Qualification, 0.9≤S < 1 are qualification, and S=1 is outstanding；
When exporting the appraisal result is to fail, corresponding rubbish image is stored to artificial screening atlas；
When the image of artificial screening atlas is marked as identifying wrong picture, identification error image is stored to convolutional Neural
The training atlas of network model carries out retraining.
Backstage is recorded in appraisal result, exercises supervision for supervision department.It is inputted by the image to system identification mistake
Retraining is carried out in convolutional neural networks model, further promotes the accuracy rate of identification model, while recording scoring knot on backstage
Fruit facilitates rubbish recycling management department to formulate Managed Solution based on the big data of appraisal result.
Referring to Fig. 5, the embodiment of the present invention also provides a kind of garbage classification evaluating apparatus based on deep learning, is based at this
In the garbage classification evaluating apparatus 1000 of deep learning, including but not limited to image acquisition unit, for obtaining the rubbish launched
Image；
Face identification unit 1100, the identity information for launching user for obtaining rubbish before garbage throwing.
Image acquisition unit 1200, for obtaining the rubbish image launched；
Image identification unit 1300, the rubbish image for will acquire are inputted into having been subjected to trained convolutional neural networks mould
Type carries out identification classification；
Score computing unit 1400, for that will identify that sorted result carries out scoring calculating, and will launch on image
The part of mistake rubbish is marked；
Display unit 1500, for exporting the appraisal result after calculating and labeled image to display module.
Error correction unit 1600, for carrying out retraining in the image input convolutional neural networks model by identification mistake.
It should be noted that by this present embodiment based on the feature deriving means that improve convolution block and it is above-mentioned based on
The feature extracting method for improving convolution block is based on identical inventive concept, and therefore, the corresponding contents in embodiment of the method are equally suitable
For present apparatus embodiment, and will not be described here in detail.
Referring to Fig. 6, the embodiments of the present invention also provide a kind of, and the garbage classification evaluating characteristic based on deep learning is extracted
Equipment should can be any type of intelligent terminal, such as handset, plate electricity based on the garbage classification evaluation 2000 of deep learning
Brain, personal computer etc..
It specifically, should include: one or more control processors based on the feature extracting device 2000 for improving convolution block
In 2100 and memory 2200, Fig. 6 by taking a control processor 2100 as an example.Control processor 2100 and memory 2200 can be with
It is connected by bus or other modes, in Fig. 6 for being connected by bus.
Memory 2200 be used as a kind of non-transient computer readable storage medium, can be used for storing non-transient software program,
Non-transitory computer executable program and module, such as the garbage classification evaluation based on deep learning in the embodiment of the present invention
Corresponding program instruction/the module of equipment.The non-transient software that control processor 2100 is stored in memory 2200 by operation
Program, instruction and module, thereby executing based on improve convolution block feature deriving means 1000 various function application and
The garbage classification evaluation method based on deep learning of above method embodiment is realized in data processing.
Memory 2200 may include storing program area and storage data area, wherein storing program area can store operation system
Application program required for system, at least one function: storage data area can be stored according to based on the feature extraction for improving convolution block
Device 1000 uses created data etc..It, can be in addition, memory 2200 may include high-speed random access memory
Including non-transient memory, a for example, at least disk memory, flush memory device or other non-transient solid-state memories.
In some embodiments, it includes the storage remotely located relative to control processor 2100 that memory 2200 is optional
Device, these remote memories can be extremely somebody's turn to do by being connected to the network based on the feature extracting device 2000 for improving convolution block.Above-mentioned net
The example of network includes but is not limited to internet, intranet, local area network, mobile radio communication and combinations thereof.
One or more above-mentioned module is stored in the memory 2200, when by one or more of controls
When processor 2100 executes, garbage classification evaluation method of one of the above method embodiment based on deep learning is executed.
The embodiment of the invention also provides a kind of computer readable storage medium, the computer-readable recording medium storage
There are computer executable instructions, which is executed by one or more control processors, for example, by Fig. 6
A control processor 2100 execute, may make said one or multiple control processors 2100 to execute above method embodiment
One of the garbage classification evaluation method based on deep learning, for example, executing the method and step Sl0 in Fig. 1 described above
To S40, the function of the device 1100 to 1600 in Fig. 5 is realized.
The apparatus embodiments described above are merely exemplary, wherein described, device can as illustrated by the separation member
It is physically separated with being or may not be, it can it is in one place, or may be distributed over multiple network dresses
It sets.Some or all of the modules therein can be selected to achieve the purpose of the solution of this embodiment according to the actual needs.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can borrow
Help software that the mode of general hardware platform is added to realize.It will be appreciated by those skilled in the art that realizing in above-described embodiment method
All or part of the process is that relevant hardware can be instructed not complete by computer program, and the program can be stored in one
In computer-readable storage medium, the program is when being executed, it may include such as the process of the embodiment of the above method.Wherein, institute
The storage medium stated can be magnetic disk, CD, read-only memory (ReadOnly Memory, ROM) or random access memory
(Random Access Memory, RAM) etc..
It is to be illustrated to preferable implementation of the invention, but the invention is not limited to above-mentioned embodiment party above
Formula, those skilled in the art can also make various equivalent variations on the premise of without prejudice to spirit of the invention or replace
It changes, these equivalent deformations or replacement are all included in the scope defined by the claims of the present application.
Claims (10)
1. a kind of garbage classification evaluation method based on deep learning, which comprises the following steps:
Obtain the rubbish image launched；
The rubbish image that will acquire inputs trained convolutional neural networks model and carries out identification classification；
It will identify that sorted result scores, and the part for having launched wrong rubbish on image will be marked；
By appraisal result and labeled image output.
2. the garbage classification evaluation method according to claim 1 based on deep learning, it is characterised in that: described to identify
Sorted result scores, obtained appraisal result specifically:
Wherein S is the appraisal result of output, and i is the serial number of rubbish classification, xiFor the quantity of the i-th class rubbish identification, piFor the i-th class
The classification accuracy of rubbish, α are preset correction factor, and the appraisal result S is exported to display module.
3. the garbage classification evaluation method according to claim 2 based on deep learning, it is characterised in that:
If exporting the appraisal result is to fail, corresponding rubbish image is stored to artificial screening atlas；
If the image of the artificial screening atlas is marked as identification error image, the identification error image is stored to institute
The training atlas of convolutional neural networks model is stated, retraining is carried out；
Backstage is recorded in the appraisal result.
4. the garbage classification evaluation method according to claim 1 based on deep learning, it is characterised in that: further include throwing
It puts and the identity information that rubbish launches user is obtained by recognition of face mode before rubbish.
5. garbage classification evaluation method according to claim 1, it is characterised in that: the convolutional neural networks model includes
Multiple convolutional layers, the first convolutional layer of the multiple convolutional layer is the Standard convolution with batchnorm (BN) and ReLU, described
Multiple convolutional layers separate convolutional layer, the depth from second convolutional layer for the depth with batchnorm (BN) and ReLU
Spending separable convolution includes depth convolution sublayer and point-by-point convolution sublayer, the multiple convolutional layer, since second convolutional layer,
Two convolutional layers of every increase, convolution kernel size increase primary.
6. a kind of garbage classification evaluating apparatus based on deep learning characterized by comprising
Image acquisition unit, for obtaining the rubbish image launched；
Image identification unit, the rubbish image for will acquire, which is inputted, to be known into having been subjected to trained convolutional neural networks model
Do not classify；
Score computing unit, for that will identify that sorted result carries out scoring calculating, and wrong rubbish will have been launched on image
Part be marked；
Display unit, for showing the appraisal result after calculating and labeled image.
7. the garbage classification evaluating apparatus according to claim 6 based on deep learning, it is characterised in that: the scoring meter
It calculates unit and specifically executes following calculating:
Wherein S is the appraisal result of output, and i is the serial number of rubbish classification, and xi is the quantity of the i-th class rubbish identification, and pi is the i-th class
The classification accuracy of rubbish, α are preset correction factor, and the appraisal result S is exported to display module.
8. the garbage classification evaluating apparatus according to claim 5 based on deep learning, it is characterised in that: further include face
Recognition unit, the identity information for launching user for obtaining rubbish before garbage throwing.
9. a kind of garbage classification valuator device based on deep learning, it is characterised in that: including at least one control processor and
Memory for being communicated to connect at least one described control processor: the memory is stored with can be by described at least one
The instruction that a control processor executes, described instruction are executed by least one described control processor so that it is described at least one
Control processor is able to carry out the garbage classification evaluation method as described in any one in claim 1-5 based on deep learning.
10. a kind of computer readable storage medium, it is characterised in that: the computer-readable recording medium storage has computer can
It executes instruction, the computer executable instructions are for making computer execute a kind of base as described in any one in claim 1-5
In the garbage classification evaluation method of deep learning.
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