CN112591333A - Automatic garbage classification device and method based on artificial intelligence - Google Patents
Automatic garbage classification device and method based on artificial intelligence Download PDFInfo
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- B65F1/00—Refuse receptacles; Accessories therefor
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Abstract
The invention provides an automatic garbage classification device based on artificial intelligence, which is characterized by comprising the following components: the garbage feeding device comprises a garbage feeding main body and a collecting device, wherein the garbage feeding main body comprises a transmission mechanism, an image collector and induction sensors, the induction sensors are arranged on two sides of the surface of the transmission mechanism, a first support is arranged on the rear side of each induction sensor, and the image collector is arranged at the top of each first support; the garbage collecting device is used for receiving the classified garbage of the garbage feeding main body, the garbage collecting device comprises a base provided with a fixing support and a collecting box body arranged around the fixing support on the base, the fixing support is provided with an executing motor, a transmission connecting rod, a rotary rod and a classification baffle, and the executing motor drives the transmission connecting rod, the rotary rod and the classification baffle to be in rotating fit to realize that the classified garbage is thrown into the corresponding collecting box body. The garbage sorting device can be used for quickly, simply and effectively sorting garbage.
Description
Technical Field
The invention relates to the field of garbage classification, in particular to an automatic garbage classification device and method based on artificial intelligence
Background
In recent years, the economy of China is rapidly developed, the living standard of people is continuously improved, the yield of household garbage is continuously increased in a large production and consumption cycle, the clearing speed is limited, and the tail end treatment faces the difficulties of high load and difficult garbage classification. Direct landfill and incineration of garbage can achieve the treatment target to a certain extent, but available resources are wasted, which is contrary to the concept of green development. The effective garbage sorting and collecting work is beneficial to the efficient recovery of resources, and has important promotion effects on environmental protection, land resource utilization and ecological civilization construction. In the existing method, manual sorting of garbage is time-consuming and labor-consuming, and the efficiency is low; the high spectrum technology is utilized to carry out sorting detection, the precision is high, the sorting effect is good, but the environmental interference resistance is poor; the mechanical arm is used for sorting with high precision and high speed, but the hardware cost is high, and the mechanism abrasion and the operation maintenance amount are large.
Therefore, there is a need for a garbage classification system with simple structure, low cost, and fast and efficient classification process to solve the above problems.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the automatic garbage classification device based on artificial intelligence, which can be used for quickly, simply and effectively automatically classifying garbage, improving the automation efficiency and relieving the manual pressure.
In order to achieve the purpose, the invention provides the following technical scheme: the utility model provides an automatic waste classification device based on artificial intelligence which characterized in that includes: the garbage feeding device comprises a garbage feeding main body and a collecting device, wherein the garbage feeding main body comprises a transmission mechanism, an image collector and induction sensors, the induction sensors are arranged on two sides of the surface of the transmission mechanism, a first support is arranged on the rear side of each induction sensor, and the image collector is arranged at the top of each first support;
the garbage collecting device is used for receiving the classified garbage of the garbage feeding main body, the garbage collecting device comprises a base provided with a fixing support and a collecting box body arranged around the fixing support on the base, the fixing support is provided with an executing motor, a transmission connecting rod, a rotary rod and a classification baffle, and the executing motor drives the transmission connecting rod, the rotary rod and the classification baffle to be in rotating fit to realize that the classified garbage is thrown into the corresponding collecting box body.
Preferably, a driving motor and a controller are arranged in the transmission mechanism, the power supply is connected with the driving motor and the controller through an extension line, the controller is used for receiving and processing images collected by the image collector, and the driving motor is used for driving the transmission mechanism.
Preferably, the controller includes: the image receiving unit is used for acquiring the image acquired by the image acquisition device; the image processing unit is used for processing the image collected by the image collector and identifying the type of an object in the image; and the first control unit is used for controlling the rotation of the transmission mechanism and sending the instruction identified by the image processing unit.
Preferably, the first control unit sets a storage variable of the recognition result for storing the history recognition data of the image.
Preferably, the executing motor is provided with a second control unit, and sends a signal to the second control unit according to the processing information of the first control unit, so that the image collector drives the classification baffle to a specified direction after identifying the garbage type.
Preferably, the collection boxes are a plurality of boxes with the same volume, and the second control unit stores position information corresponding to the collection boxes.
Preferably, the controller comprises a spam image classification model comprising the following learning process:
a. collecting a household garbage image to form a household garbage image data set;
b. b, sending the data set in the step a into a neural network for model training to form a household garbage image recognition model; in the training process of the network model, after the network model is trained for one epoch, carrying out model verification once; and testing the network model by using the test set after the training is finished.
Preferably, the algorithm model of the garbage image classification model is one of an inclusion v3 algorithm model, an SVM algorithm model, a VGG algorithm model and a ResNet-50 algorithm model.
Preferably, the inductive sensor is a sensor with obstacle induction, and the inductive sensor is one of a photoelectric sensor and an ultrasonic equidistant sensor.
In another aspect, the present invention provides a method for automatic garbage classification based on artificial intelligence, wherein the method is performed in the automatic garbage classification device, and the method comprises the following steps:
s1, delivering the garbage to the transmission mechanism at intervals;
s2, triggering an image collector to collect images by the garbage through inductive sensors on two sides of the transmission mechanism;
and S3, calculating the time of feeding the conveying mechanism to the classification baffle according to the feeding interval time of S1 and the sensing time of the S2 sensor, setting the optimal rotation angle calculation of the classification baffle according to the positions of different types of garbage marked by the collecting box body, determining the rotation strategy of the actuating motor, setting the corresponding relation of garbage types and realizing the automatic classification of the garbage.
Through the technical scheme, the invention has the following advantages:
the garbage collection device disclosed by the invention has the advantages that the garbage is identified through an accurate identification technology, the garbage after identification is classified correspondingly through the collection device, the accurate, quick and effective classification is realized, the manual classification burden is reduced, the recovery efficiency is effectively improved, the classified recovery of the household garbage is facilitated, the environmental pollution is reduced, and the resource recycling is realized.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention.
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a schematic mechanical diagram of the collection device of the present invention;
FIG. 3 is a block diagram of the control board of the present invention;
FIG. 4 is a block diagram of the actuator motor connection of the present invention;
FIG. 5 is a schematic view of an embodiment of the present invention;
FIG. 6 is a diagram of the effect of image enhancement techniques used in the automatic garbage classification method of the present invention;
FIG. 7 is a diagram of a network architecture using the ResNet-50 algorithm model in accordance with a preferred embodiment of the present invention;
FIG. 8 is a flow chart of a preferred embodiment of the present invention providing an automatic garbage classification method;
FIG. 9 is a graph of learning rate rules corresponding to the cosine annealing learning rate strategy used in the optimization technique of the automatic garbage classification method of the present invention.
Description of the reference numerals
10. Garbage feeding main body 205 and classification baffle
101. Transmission 206, second control unit
102. First bracket 207 and collection box body
103. Image collector 208 and execution motor
104. Inductive sensor 301 and drive motor
20. Collection device 302, controller
201. Base 303 and image receiving unit
202. Fixing support 304 and image processing unit
203. Transmission connecting rod 305 and first control unit
204. Rotary rod
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
In the present invention, the use of directional terms such as "upper, lower, left, right" generally means upper, lower, left, right as viewed with reference to the accompanying drawings, unless otherwise specified; "inner and outer" refer to the inner and outer relative to the profile of the components themselves. The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
The invention will now be further illustrated with reference to the following examples, without thereby being restricted thereto.
Referring to fig. 1, the present invention provides an automatic garbage classification apparatus based on artificial intelligence, comprising: the garbage feeding main body 10 comprises a transmission mechanism 101, an image collector 103 and induction sensors 104, the induction sensors 104 are arranged on two sides of the surface of the transmission mechanism 101, a first support 102 is arranged on the rear side of each induction sensor 104, and the image collector 103 is arranged at the top of each first support 102;
the collecting device 20 is used for receiving the classified garbage of the garbage feeding main body 10, the collecting device 20 includes a base 201 provided with a fixing support 202 and a collecting box 207 arranged around the fixing support 202 on the base 201, the fixing support 202 is configured with an actuating motor 208, a transmission connecting rod 203, a rotating rod 204 and a classification baffle 205, and the actuating motor 208 drives the transmission connecting rod 203, the rotating rod 204 and the classification baffle 205 to be matched in a rotating manner so as to realize that the classified garbage is thrown into the corresponding collecting box 207.
Specifically, pour rubbish interval into drive mechanism 101 is last, through inductive sensor 104 perception rubbish gets into image collector 103's identification area carries out the identification process to rubbish, falls into after carrying out the identification process to rubbish on categorised baffle 205, through executive motor 208 is right transmission connecting rod 203 the rotary rod 204 with the normal running fit of categorised baffle 205 puts in rubbish to corresponding in the collection box 207, carry out quick effectual waste classification, can effectually classify the recovery to domestic waste, reduce environmental pollution, realize the reuse of resource.
Specifically, when the garbage is thrown into the transmission mechanism 101, the garbage is vibrated or dried by the feeding device, so that the garbage can be kept as an individual body relatively when the garbage is thrown into the transmission mechanism, the garbage cannot be sticky together, the garbage recognition capability of the image collector 103 is enhanced, the garbage recognition success rate is effectively improved, the recognition error rate is reduced, and the transmission connecting rod 203 mounted on the execution motor 208 can be lifted and adjusted in angle on the classification baffle 205.
According to a preferred embodiment of the present invention, a driving motor 301 and a controller 303 are disposed in the transmission mechanism 101, the driving motor 301 is connected to the controller 303 through an extension line, the controller 302 is connected to the image collector 103, and the driving motor 301 is configured to drive the transmission mechanism 101.
According to a preferred embodiment of the present invention, the controller 302 includes: an image receiving unit 303, configured to obtain an image acquired by the image acquirer 103; an image processing unit 304, configured to process the image acquired by the image acquirer 103, and identify a type of an object in the image; a first control unit 305 for controlling the rotation of the transmission mechanism 101 and processing the instruction identified by the image processing unit 304, wherein the first control unit 305 sets a storage variable of the identification result for storing the historical identification data of the image.
Specifically, it can be understood that an image receiving unit 303, an image processing unit 304, and a first control unit 305 are disposed in the controller 302, and after the image receiving unit 303 and the image processing unit 304 recognize the spam image information, the first control unit 305 performs a classification process on the spam image.
According to a preferred embodiment of the present invention, the execution motor 208 is provided with a second control unit 206, and the first control unit 305 sends a signal of processing information to the second control unit 206, so that the image collector 103 drives the sorting flapper 205 to a specific direction after identifying the garbage type.
According to a preferred embodiment of the present invention, the collection box 207 is a plurality of boxes with the same volume, and the second control unit 206 stores position information corresponding to the collection box 207.
According to a preferred embodiment of the present invention, the inductive sensor 104 is a sensor with obstacle sensing function, and the inductive sensor 104 is one of a photoelectric sensor and an ultrasonic equidistant sensor.
The invention also provides an automatic garbage classification method based on artificial intelligence, which is carried out in the automatic garbage classification device based on artificial intelligence;
s1, delivering the garbage to the transmission mechanism 101 at intervals;
s2, triggering an image collector 104 by garbage through the inductive sensors 103 on the two sides of the transmission mechanism 101 to collect images;
s3, calculating the time of feeding the classification baffle 205 on the transmission mechanism 101 according to the feeding interval time of S1 and the sensing time of the sensing sensor 104 of S2, setting the optimal rotation angle calculation of the classification baffle 205 according to the positions of different types of garbage marked by the collection box 207, determining the rotation strategy of the execution motor 208, setting the corresponding relation of garbage types and realizing the automatic classification of the garbage.
According to a preferred embodiment of the present invention, the controller 302 comprises a garbage image classification model, which comprises the following learning process:
a. collecting a household garbage image to form a household garbage image data set;
b. b, sending the data set in the step a into a neural network for model training to form a household garbage image recognition model; in the training process of the network model, after the network model is trained for one epoch, carrying out model verification once; testing the network model by using the test set after training;
reading a video frame image of the image collector 103, and transmitting the frame image to the trained network model; and the video frame is used as an input layer of a network model to perform neural network calculation, and the category ID and the probability value of the video frame are obtained on an output layer of the network model.
Specifically, the source of the household garbage image in the household garbage image data set is a network photo, a real-life photo and a household garbage picture obtained by intercepting other data sets.
According to the invention, preferably, the images of the domestic waste in the data set are randomly augmented by image enhancement techniques to expand the number of samples in the data set.
According to the invention, preferably, the training images are expanded by using an image enhancement technology, so that the recognition anti-interference capability is enhanced; more preferably, the image enhancement techniques include random flipping, random brightness, and random cropping; FIG. 6 is a diagram illustrating the effect of image enhancement techniques used in the automatic garbage classification method of the present invention.
According to the invention, preferably, for high-precision and fast image recognition, the algorithm model of the garbage image classification model is one of an inclusion v3 algorithm model, an SVM algorithm model, a VGG algorithm model and a ResNet-50 algorithm model; preferably, the ResNet algorithm models include ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152, and other algorithm models; the ResNet algorithm model adopts the idea of identity mapping, if the network does not need deeper depth, the network can walk the road of identity mapping and set the residual mapping as 0, so that data is not forced to pass through the very deep network, and the propagation efficiency of the gradient is accelerated; the method is more preferably a ResNet-50 algorithm model, the ResNet-50 algorithm model is high in identification precision and less in consumed time, real-time performance and accuracy of the algorithm are guaranteed, and the method is more suitable for the automatic garbage classification method.
According to the invention, preferably, an automatic garbage classification method is developed based on a deep learning technology architecture, fig. 7 is a network structure diagram of a ResNet-50 algorithm adopted by the invention, and the gradient propagation efficiency is enhanced by adopting a residual error connection idea.
According to the present invention, preferably, fig. 8 is a flowchart of an automatic garbage classification method provided by the present invention, and a ResNet-50 algorithm model is adopted as a garbage image classification model, the method includes the following steps:
s1, collecting a household garbage image to form a household garbage image data set;
s2, performing one-hot coding on the data samples in the data set in the step S1, and sending the data samples into a deep convolutional neural network ResNet-50 for model training to form a household garbage image classification model; in the training process of the garbage image classification model, after the garbage image classification model is trained for an epoch, the model is verified; testing the garbage image classification model by using the test set after the training is finished;
s3, reading a video frame image of the image acquisition equipment, and transmitting the frame image to the garbage image classification model trained in the step S2; the video frame is used as an input layer of a garbage image classification model, neural network calculation is carried out, and the category ID and the probability value of the video frame are obtained on an output layer of the garbage image classification model;
and S4, setting the corresponding relation of the garbage categories to realize automatic classification of the garbage.
According to the present invention, preferably, all the garbage images are scaled to a uniform size, e.g. 224 x 224 pixels, according to the selected ResNet-50 garbage image classification model, and ResNet has 4 down-samples, each with a sampling step size of 2, just to get an integer size.
According to the present invention, preferably, in step S2, the data set is divided into a training set, a validation set, and a test set; the training set is used for autonomous learning of the network, and the verification set is used for verifying whether the model has an overfitting phenomenon; the test set is used to test the final accuracy of the model.
According to the present invention, in step S2, in order to constrain the training network and optimize the neural network parameters, the garbage image classification model further includes a loss function, preferably, the loss function is a cross-entropy loss function, where the cross-entropy loss function is divided into p (x) and q (x), p (x) represents the real encoding of the data, q (x) represents the prediction result after being output by softmax, and the cross-entropy loss function is as follows:
the smaller the cross entropy of the cross entropy loss function is, the closer the probability distribution of the predicted value and the true value is, and the loss function is used for constraining the training network, thereby being beneficial to optimizing parameters of the neural network.
According to the invention, in the training process of the garbage image classification model, the network optimization speed is slow due to an excessively small learning rate, and the network swings around an excellent value due to an excessively large learning rate, so that the network cannot be converged. According to a preferred embodiment of the present invention, the learning rate is dynamically adjusted using a cosine annealing algorithm. The rule of the cosine annealing learning rate is as follows:
in the expression: i is an index value of the number of training times,
and
respectively representing the maximum and minimum values of the learning rate, defining a range of learning rates, TcurIndicates the currently executed epoch number, TiIs to set the total number of epochs for training. In the cosine function, the cosine value slowly decreases, then rapidly decreases, and then slowly decreases as x increases. The descending mode is very suitable for the descending of the learning rate, the learning rate of the descending mode is periodically changed along with the change of the number of training rounds,the model is enabled to jump out of the current local optimal solution, and then a solution superior to the current solution is found, so that the precision of the model is improved. As shown in fig. 9, a learning rate rule graph corresponding to the cosine annealing learning rate strategy of the present invention is shown. By adopting the technical scheme, the training accuracy can be improved, and meanwhile, the generalization performance of the model is enhanced.
According to a preferred embodiment of the present invention, the training process of the garbage image classification model further includes an optimization strategy of transfer learning. The transfer learning process is as follows:
a. downloading a training model of ResNet-50 in ImageNet given by an official party, and deleting a global average pooling layer and a full connection layer at the tail end of the training model;
b. storing the processed model, and freezing all network layer parameters of the current model;
c. and adding a global average pooling layer and a full-connection layer at the tail end of the current model, setting network parameters of the two layers to be trainable, and setting the number of nodes of the full-connection layer with classification effect as the number of garbage categories.
The data scale of the garbage image classification problem belongs to small scale (training samples are below 10 k), and the weight of the model trained on a large data set is pre-loaded, namely, the model is subjected to transfer learning, so that the training accuracy can be improved, and meanwhile, the generalization performance of the model is enhanced.
According to the present invention, preferably, in the step b, the category ID is determined by the category of the household garbage included in the data set, so that the corresponding relationship of the garbage categories is set according to actual requirements, and further, the garbage is automatically classified.
According to the invention, preferably, the training process of the garbage image classification model further comprises the step of accelerating training by using hardware, so that the training speed of the garbage image classification model is increased; more preferably, the hardware comprises one or more of a GPU, TPU, intel neuro stick.
Example 1
As shown in fig. 5, after the garbage is primarily screened by the device, the garbage is put on the conveying mechanism 101 at intervals, the garbage is conveyed by the conveying mechanism 101, passes through the area of the induction sensor 104, after the induction sensor 104 senses that an object passes through, an electric signal is sent to the image collector 103 through the controller 302 to perform image recognition classification and normalization on the garbage on the conveying mechanism 101, according to the type of the garbage recognition of the image processing unit, the first control unit 305 sends a corresponding instruction to enable the second control unit 206 to select a rotation strategy for recognizing the type of the garbage, the garbage can be effectively put into the corresponding collecting box 207 after reaching the classification baffle 205 through the execution of the execution motor 208, and the classification baffle 205 returns to the original position for next rotary delivery after the delivery is completed.
The preferred embodiments of the present invention in the form described above in detail with reference to the accompanying drawings, however, the present invention is not limited thereto. Within the scope of the technical idea of the invention, numerous simple modifications can be made to the technical solution of the invention, including combinations of the specific features in any suitable way, and the invention will not be further described in relation to the various possible combinations in order to avoid unnecessary repetition. Such simple modifications and combinations should be considered within the scope of the present disclosure as well.
It should be noted that, in the foregoing embodiments, various features described in the foregoing embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various possible combinations are not described in further detail in the embodiments of the present invention.
In addition, any combination of various different implementation manners of the embodiments of the present invention is also possible, and the embodiments of the present invention should be considered as disclosed in the embodiments of the present invention as long as the combination does not depart from the spirit of the embodiments of the present invention.
Claims (10)
1. The utility model provides an automatic waste classification device based on artificial intelligence which characterized in that includes: the garbage feeding device comprises a garbage feeding main body (10) and a collecting device (20), wherein the garbage feeding main body (10) comprises a transmission mechanism (101), an image collector (103) and an induction sensor (104), the induction sensor (104) is arranged on two sides of the surface of the transmission mechanism (101), a first support (102) is arranged on the rear side of the induction sensor (104), and the image collector (103) is arranged at the top of the first support (102);
the garbage collecting device (20) is used for receiving classified garbage of the garbage feeding main body (10), the garbage collecting device (20) comprises a base (201) provided with a fixing support (202) and a collecting box body (207) arranged around the fixing support (202) on the base (201), the fixing support (202) is provided with an executing motor (208), a transmission connecting rod (203), a rotating rod (204) and a classifying baffle (205), and the executing motor (208) drives the transmission connecting rod (203), the rotating rod (204) and the classifying baffle (205) to be in rotating fit to achieve that the classified garbage is thrown into the corresponding collecting box body (207).
2. The automatic garbage sorting device according to claim 1, wherein the transmission mechanism (101) is provided with a driving motor (301) and a controller (302), the controller (302) is connected with the driving motor (301) through an extension line, the controller (302) is used for receiving and processing the images collected by the image collector (103), and the driving motor (301) is used for driving the transmission mechanism (101).
3. The automatic waste sorting device according to claim 1 or 2, wherein the controller (302) comprises:
an image receiving unit (303) for acquiring the image acquired by the image acquirer (103);
the image processing unit (304) is used for processing the image acquired by the image acquirer (103) and identifying the type of an object in the image;
the first control unit (305) is used for controlling the rotation of the transmission mechanism (101) and sending the instruction identified by the image processing unit (304).
4. An automatic garbage classification apparatus according to any one of claims 1-3, characterized in that the first control unit (305) sets a storage variable of the recognition result for storing historical recognition data of the image.
5. The automatic waste sorting device according to any one of claims 1-4, characterized in that the execution motor (208) is provided with a second control unit (206), and the processing information of the first control unit (305) sends a signal to the second control unit (206) for the image collector (103) to identify the waste type and then drive the sorting barrier (205) to a specified direction.
6. The automatic waste sorting device according to any one of claims 1-5, characterised in that the collection bin (207) is a plurality of bins of equal volume, and in that the second control unit (206) has stored therein position information corresponding to the collection bin (207).
7. The automatic garbage classification apparatus according to any one of claims 1-6, wherein the controller (302) comprises a garbage image classification model comprising the following learning process:
a. collecting a household garbage image to form a household garbage image data set;
b. b, sending the data set in the step a into a neural network for model training to form a household garbage image recognition model; in the training process of the network model, after the network model is trained for one epoch, carrying out model verification once; and testing the network model by using the test set after the training is finished.
8. The automatic garbage classification device according to any one of the claims 1-7, wherein the algorithm model of the garbage image classification model is one of an inclusion v3 algorithm model, an SVM algorithm model, a VGG algorithm model, and a ResNet-50 algorithm model.
9. The automatic waste sorting device according to any one of claims 1-8, wherein the inductive sensor (104) is a sensor with obstacle sensing, and the inductive sensor (104) is one of a photoelectric sensor and an ultrasonic equidistant sensor.
10. A method for artificial intelligence based automatic garbage classification, characterized in that the method is performed in an artificial intelligence based automatic garbage classification apparatus according to any of claims 1-9, comprising the steps of:
s1, delivering the garbage to the transmission mechanism (101) at intervals;
s2, triggering an image collector (103) by garbage through inductive sensors (104) on two sides of a transmission mechanism (101) to collect images;
s3, calculating the time of feeding the sorting baffle (205) on the transmission mechanism (101) according to the feeding interval time of S1 and the sensing time of the S2 sensing sensor (104), setting the optimal rotation angle calculation of the sorting baffle (205) according to the positions of different types of garbage marked by the collecting box body (207), determining the rotation strategy of the execution motor (208) according to the optimal rotation angle calculation, setting the corresponding relation of garbage types, and realizing the automatic sorting of the garbage.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011459890.4A CN112591333A (en) | 2020-12-11 | 2020-12-11 | Automatic garbage classification device and method based on artificial intelligence |
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