CN111332651A - Photoelectric sensing garbage bin based on degree of depth study - Google Patents
Photoelectric sensing garbage bin based on degree of depth study Download PDFInfo
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- CN111332651A CN111332651A CN202010181584.2A CN202010181584A CN111332651A CN 111332651 A CN111332651 A CN 111332651A CN 202010181584 A CN202010181584 A CN 202010181584A CN 111332651 A CN111332651 A CN 111332651A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F1/00—Refuse receptacles; Accessories therefor
- B65F1/14—Other constructional features; Accessories
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F1/00—Refuse receptacles; Accessories therefor
- B65F1/0033—Refuse receptacles; Accessories therefor specially adapted for segregated refuse collecting, e.g. receptacles with several compartments; Combination of receptacles
- B65F1/004—Refuse receptacles; Accessories therefor specially adapted for segregated refuse collecting, e.g. receptacles with several compartments; Combination of receptacles the receptacles being divided in compartments by partitions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F1/00—Refuse receptacles; Accessories therefor
- B65F1/14—Other constructional features; Accessories
- B65F1/16—Lids or covers
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F2210/00—Equipment of refuse receptacles
- B65F2210/128—Data transmitting means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F2210/00—Equipment of refuse receptacles
- B65F2210/138—Identification means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F2210/00—Equipment of refuse receptacles
- B65F2210/165—Remote controls
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F2210/00—Equipment of refuse receptacles
- B65F2210/168—Sensing means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F2210/00—Equipment of refuse receptacles
- B65F2210/20—Temperature sensing means
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- Mechanical Engineering (AREA)
- Processing Of Solid Wastes (AREA)
Abstract
The invention discloses a photoelectric sensing garbage can based on deep learning, which comprises a garbage can main body, a main control board and a mechanical device, wherein the main control board comprises a single chip microcomputer and an external device, the single chip microcomputer realizes information transmission with the external device through a wired communication protocol, the mechanical device comprises a touch device, an identification device and a compression device, the garbage can main body comprises an upper can cover, a partition plate and a lower can body, the partition plate is arranged between the upper can cover and the lower can body, the upper can cover and the lower can body are respectively and fixedly connected with an external connecting rod, a first installation chamber is arranged at the upper end of the external connecting rod, a second installation chamber is arranged in the middle of the external connecting rod, a GPRS device is arranged in the second installation chamber, the partition plate is connected with the external connecting rod through a first steering engine, the first steering engine controls the partition plate to open and. The invention realizes intelligent recognition and classification of garbage, greatly reduces labor cost, solves the problem of difficult classification in the prior art, and simultaneously realizes full-automatic intelligent recovery of garbage.
Description
Technical Field
The invention relates to a photoelectric sensing garbage can based on deep learning, and belongs to the technical field of public health.
Background
Garbage classification generally refers to a series of activities that store, place, and transport garbage according to a certain rule or standard, and then convert the garbage into public resources. The domestic garbage management regulation of Shanghai city in 7 months in 2019 is formally implemented, the domestic garbage management regulation is taken as Shanghai where a garbage classification test point is first implemented, the overall effect of garbage classification is far better than expected, the occupied land is reduced, the pollution is reduced, waste is changed into valuable, and the performance is good. It is expected that garbage classification is an irreversible trend in social development. With the continuous change of social demands, various commodities are shown, and the types of generated garbage are also eight-door with five flowers, so that the difficulty of garbage classification is increased. It is difficult for general people to accurately master the garbage classification method, so people are inevitably exposed and difficult to color when classifying and putting.
Most of the existing intelligent garbage cans adopt an image recognition technology and a voice guidance function to guide people to classify and put garbage, but many people are inevitable to be uncoordinated in life, so that the garbage classification purpose is lost. Even if the recording penalty of the person who is thrown randomly through image recognition is only temporary and permanent. Due to the fact that the people flow rate of various occasions is different, along with the fact that the demand degrees of uncertain factors (such as taking action) on the trash cans are different, when the trash is recycled, workers often have the situation that the trash cans are not full or overflow, and waste is caused to manpower resources. Some garbage such as paper boxes, foams and the like occupy large space, and the garbage can cannot be better utilized under normal conditions, so that material resources are wasted. Along with the concept of energy conservation, people feel deepened gradually, and if the intelligent garbage can is always in a working state, energy waste can be caused.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a photoelectric sensing garbage can based on deep learning, which realizes intelligent recognition and classification of garbage, greatly reduces labor cost, solves the problem of difficult classification in the prior art, and simultaneously realizes full-automatic intelligent recovery of the garbage.
The invention mainly adopts the technical scheme that:
a photoelectric sensing garbage can based on deep learning comprises a garbage can main body, a main control board and a mechanical device, wherein the main control board comprises a single chip microcomputer and an external device, the single chip microcomputer realizes information transmission with the external device through a wired communication protocol, the mechanical device comprises a touch device, an identification device and a compression device, the garbage can main body comprises an upper can cover, a partition plate and a lower can body, the partition plate is arranged between the upper can cover and the lower can body, the upper can cover and the lower can body are respectively and fixedly connected with an external connecting rod, a first installation chamber is arranged at the upper end of the external connecting rod, a second installation chamber is arranged in the middle of the external connecting rod, the partition plate is connected with the external connecting rod through a first steering engine, the partition plate is controlled to be opened and closed by the first steering engine, and the first steering engine is controlled;
the identification device comprises a camera, an identification platform and a photoresistor-laser head sensor, the identification platform is installed at the bottom of the upper barrel cover through a second steering engine and is in control connection with the single chip microcomputer, the camera is installed at the top end inside the upper barrel cover and is located above the identification platform, the photoresistor-laser head sensor is installed on the side edge of the identification platform, the camera and the photoresistor-laser head sensor are in information transmission connection with the single chip microcomputer respectively, and the single chip microcomputer is installed at the bottom of the upper barrel cover;
the compression device comprises an ultrasonic sensor and a compression plate, the ultrasonic sensor and the compression plate are respectively installed at the bottom of the upper barrel cover, the ultrasonic sensor is in information transmission connection with the single chip microcomputer, the compression plate is in control connection with the single chip microcomputer, and the ultrasonic sensor transmits information to a corresponding computer end or mobile phone end through a GPRS device and is used for informing workers of garbage recovery;
the touch device comprises a pyroelectric sensor, the pyroelectric sensor is arranged in a first installation chamber, and the pyroelectric sensor is in information transmission connection with the single chip microcomputer;
the lower barrel body is divided into four independent barrel bodies uniformly around the long shaft, and the four independent barrel bodies are connected with the long shaft through connecting rods respectively.
Preferably, the outer side wall of each independent barrel body in the lower barrel body is provided with a cleaning door and a button, the cleaning door is provided with a handle, the bottom end of the long shaft is connected with the fixed end of an electric push rod, the electric push rod is in control connection with the button, and the telescopic end of the electric push rod is used for pushing garbage in the independent barrel body.
Preferably, the top end in the upper barrel cover is further provided with an illumination sensor and a background lamp, the illumination sensor and the background lamp are located above the identification platform, the illumination sensor is in information transmission connection with the single chip microcomputer, and the single chip microcomputer is in control connection with the background lamp.
Preferably, the outer side wall of the lower barrel body is further provided with an LED lamp, and the LED lamp is in control connection with the single chip microcomputer.
Preferably, the cross section of the compression plate is one fourth of the cross section of the lower barrel body.
Preferably, the single chip microcomputer identifies the image acquired by the camera by operating a deep learning module, the deep learning module comprises a data set and a convolutional neural network, the data set comprises an open source garbage picture data set or a garbage picture data set acquired by a user, and the picture is preprocessed before training so that the parameters of the picture meet the requirements of the convolutional neural network; the convolutional neural network is a feedforward neural network which comprises convolutional calculation and has a deep structure, an activation function and a pooling layer are arranged behind a convolutional layer, and the output of a final full-connection layer is classified by using Softmax.
Has the advantages that: the invention provides a photoelectric sensing garbage can based on deep learning, which has the following advantages compared with the prior art:
(1) the invention uses the illuminance sensor to realize the detection of the ambient light intensity, so that the LED is controlled to be stabilized at a constant illuminance, the garbage identification is facilitated, and the device is widely suitable for various occasions and has strong adaptability.
(2) The deep learning module has great advantages of utilizing the convolutional neural network to classify pictures, training a deep learning model by using the conventional deep learning frame, and storing the trained model as an H5 file which can be called by a singlechip.
(3) The garbage compression device is provided with the compression device and the touch device, so that garbage can be compressed in time, and the space utilization rate is improved; the information can be transmitted to the recycling personnel in real time, so that the labor waste is reduced; the garbage can be in a dormant state when not needed, and resource waste is reduced.
Drawings
FIG. 1 is a diagrammatic view of the trash can of the present invention;
FIG. 2 is a perspective view of the compression device;
FIG. 3 is a side view of the recycling apparatus;
fig. 4 is a work flow chart of the photoelectric sensing trash can based on deep learning.
In the figure: the intelligent cleaning barrel comprises an upper barrel cover 1, a illuminance sensor 1-1, a partition plate 2, a lower barrel body 3, a third steering engine 3-1, a long shaft 3-2, an independent barrel body 3-3, a connecting rod 3-4, a cleaning door 3-5, a button 3-6, a handle 3-7, an electric push rod 3-8, an LED lamp 3-9, an outer connecting rod 4, a first installation chamber 4-1, a second installation chamber 4-2, a single chip microcomputer 5, a first steering engine 6, a GPRS device 7, a camera 8-1, an identification platform 8-2, a photoresistor-laser head sensor 8-3, a second steering engine 8-4, an ultrasonic sensor 9-1, a compression plate 9-2 and a pyroelectric sensor 11.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
A photoelectric sensing garbage can based on deep learning comprises a garbage can main body, a main control board and a mechanical device, wherein the main control board comprises a single chip microcomputer and an external device, the single chip microcomputer 5 realizes information transmission with the external device through a wired communication protocol, the mechanical device comprises a touch device, an identification device and a compression device, the garbage can main body comprises an upper can cover 1, a partition plate 2 and a lower can body 3, the partition plate 2 is arranged between the upper can cover 1 and the lower can body 3, the upper can cover 1 and the lower can body 3 are respectively and fixedly connected with an external connecting rod 4, a first installation chamber 4-1 is arranged at the upper end of the external connecting rod 4, a second installation chamber 4-2 is arranged in the middle of the external connecting rod 4, a GPRS device 7 is arranged in the second installation chamber 4-2, the partition plate 2 is connected with the external connecting rod 4 through a first steering engine 6, and the partition plate 2 is controlled to open and close by the, the first steering engine 6 is in control connection with the singlechip 5;
the identification device comprises a camera 8-1, an identification platform 8-2 and a photoresistance-laser head sensor 8-3, wherein the identification platform 8-2 is installed at the bottom of the upper barrel cover 1 through a second steering engine 8-4, the second steering engine 8-4 is in control connection with the single chip microcomputer 5, the camera 8-1 is installed at the top end inside the upper barrel cover 1 and is located above the identification platform 8-2, the photoresistance-laser head sensor 8-3 is installed on the side edge of the identification platform 8-2, the camera 8-1 and the photoresistance-laser head sensor 8-3 are in information transmission connection with the single chip microcomputer 5 respectively, and the single chip microcomputer 5 is installed at the bottom of the upper barrel cover 1;
the compression device comprises an ultrasonic sensor 9-1 and a compression plate 9-2, the ultrasonic sensor 9-1 and the compression plate 9-2 are respectively installed at the bottom of the upper barrel cover 1, the ultrasonic sensor 9-1 is in information transmission connection with the single chip microcomputer 5, the compression plate 9-2 is in control connection with the single chip microcomputer 5, and the ultrasonic sensor 9-1 transmits information to a corresponding computer end or a corresponding mobile phone end through a GPRS device 7 for informing a worker of garbage recovery;
the touch device comprises a pyroelectric sensor 11, the pyroelectric sensor 11 is installed in the first installation chamber 4-1, and the pyroelectric sensor 11 is in information transmission connection with the single chip microcomputer 5;
the center of the bottom end in the lower barrel body 3 is provided with a third steering engine 3-1, an output shaft of the third steering engine 3-1 is connected with the bottom end of a vertically arranged long shaft 3-2, the lower barrel body 3 is uniformly divided into four independent barrel bodies 3-3 around the long shaft 3-2, and the four independent barrel bodies 3-3 are respectively connected with the long shaft 3-2 through connecting rods 3-4.
Preferably, the outer side wall of each independent barrel body 3-3 in the lower barrel body 3 is provided with a cleaning door 3-5 and a button 3-6, the cleaning door 3-5 is provided with a handle 3-7, the bottom end of the long shaft 3-2 is connected with the fixed end of an electric push rod 3-8, the electric push rod 3-8 is in control connection with the button 3-6, and the telescopic end of the electric push rod 3-8 is used for pushing garbage in the independent barrel body 3-3.
Preferably, the top end in the upper barrel cover 1 is further provided with a light intensity sensor 1-1 and a background light, the light intensity sensor 1-1 and the background light are located above the identification platform 8-2, the light intensity sensor 1-1 is in information transmission connection with a single chip microcomputer 5, and the single chip microcomputer 5 is in control connection with the background light.
Preferably, the outer side wall of the lower barrel body 3 is further provided with LED lamps 3-9, and the LED lamps 3-9 are in control connection with the single chip microcomputer 3-7.
Preferably, the cross-section of the compression plate 9-2 is one quarter of the cross-section of the lower tub 3, i.e., corresponds to the cross-section of the single independent tub 3-3.
Preferably, the single chip microcomputer 5 identifies an image acquired by the camera by operating a deep learning module, the deep learning module comprises a data set and a convolutional neural network, the data set comprises an open source garbage image data set or a garbage image data set acquired by a user, and the image is preprocessed before training so that parameters of the image meet the requirements of the convolutional neural network; the convolutional neural network is a feedforward neural network which comprises convolutional calculation and has a deep structure, an activation function and a pooling layer are arranged behind a convolutional layer, and the output of a final full-connection layer is classified by using Softmax.
In the invention, the illuminance sensor 1-1 utilizes the characteristic that the reverse current of the photosensitive diode changes along with the illuminance to realize the detection of the ambient light intensity, then feeds back information to the singlechip 5, and controls the ambient light to be stabilized at a constant illuminance by adjusting the brightness of the background light (the position of the background light is consistent with the illuminance sensor) through the singlechip 5, thus being beneficial to the identification of garbage. The photoresistor-laser head sensor 8-3 detects whether garbage is put into the recognition platform or not by utilizing the photoconductive effect in the internal photoelectric effect of the photoresistor. The ultrasonic sensor 9-1 is arranged beside the compression device and realizes the monitoring of the garbage capacity in various garbage cans depending on the rotation of the lower can body.
As shown in fig. 4, the working flow of the present invention is as follows:
step 1: if the pyroelectric sensor 11 senses that a human body approaches the garbage can, the detected information is transmitted to the single chip microcomputer 5, the single chip microcomputer 5 controls the first steering engine 6 to rotate, so that the partition plate 2 is opened, the upper part and the lower part of the garbage can are communicated, and otherwise, the system does not act;
step 2: when the partition plate 2 is opened, the garbage can is wakened up, and when the photoresistor-laser head sensor 8-3 detects that garbage is put into the identification platform 8-2, detected information is transmitted to the single chip microcomputer 5;
and step 3: the singlechip 5 controls the illumination sensor 1-1 to be started for detecting the intensity of ambient light and feeding detection information back to the singlechip 5, the singlechip 5 adjusts the brightness of a background light to control the ambient light to be stabilized at a constant illumination, so that garbage identification is facilitated, and meanwhile, the camera 8-1 acquires images of garbage placed on the identification platform 8-2 and transmits the acquired images to the singlechip 5;
and 4, step 4: the single chip microcomputer identifies and classifies the acquired images through the deep learning module, the single chip microcomputer 5 controls the third steering engine 3-1 to rotate according to the classification result, so that the corresponding classified independent barrel bodies 3-3 rotate to the position below the identification platform 8-2, and at the moment, the single chip microcomputer 5 controls the second steering engine 8-4 to rotate, so that the identification platform 8-2 stands up to finish the correct garbage throwing;
and 5: the ultrasonic sensor 9-1 is used for detecting the content of the garbage in the garbage can, when the content of the garbage reaches the maximum value, the ultrasonic sensor 9-1 transmits detected information to the single chip microcomputer 5, the single chip microcomputer controls the third steering engine to rotate, so that the independent can body 3-3 filled with the garbage rotates to the position below the compression plate 9-2, and meanwhile, the single chip microcomputer 5 controls the compression plate 9-2 to compress downwards;
step 6: when the ultrasonic sensor 9-1 detects that the volume of the compressed garbage reaches the maximum limit, the ultrasonic sensor transmits information to the singlechip 5, the singlechip 5 controls the LED lamps 3-9 to be lightened, and the information is transmitted to the corresponding computer end or mobile phone end through the GPRS device 7 to inform a worker to recover the information;
and 7: when the workers recover, the cleaning door 3-5 is opened through the handle 3-7, the button 3-6 is pressed at the same time, and the electric push rod 3-8 is controlled by the button 3-6, so that the garbage is pushed out, and the recovery is convenient.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (6)
1. A photoelectric sensing garbage can based on deep learning is characterized by comprising a garbage can main body, a main control board and a mechanical device, wherein the main control board comprises a single chip microcomputer and an external device, the single chip microcomputer realizes information transmission with the external device through a wired communication protocol, the mechanical device comprises a touch device, an identification device and a compression device, the garbage can main body comprises an upper can cover, a partition plate and a lower can body, the partition plate is arranged between the upper can cover and the lower can body, the upper can cover and the lower can body are respectively and fixedly connected with an external connecting rod, a first installation chamber is arranged at the upper end of the external connecting rod, a second installation chamber is arranged in the middle of the external connecting rod, a GPRS device is arranged in the second installation chamber, the partition plate is connected with the external connecting rod through a first steering engine, the partition plate is controlled to open and close through the first steering engine, and the first steering engine is;
the identification device comprises a camera, an identification platform and a photoresistor-laser head sensor, the identification platform is installed at the bottom of the upper barrel cover through a second steering engine and is in control connection with the single chip microcomputer, the camera is installed at the top end inside the upper barrel cover and is located above the identification platform, the photoresistor-laser head sensor is installed on the side edge of the identification platform, the camera and the photoresistor-laser head sensor are in information transmission connection with the single chip microcomputer respectively, and the single chip microcomputer is installed at the bottom of the upper barrel cover;
the compression device comprises an ultrasonic sensor and a compression plate, the ultrasonic sensor and the compression plate are respectively installed at the bottom of the upper barrel cover, the ultrasonic sensor is in information transmission connection with the single chip microcomputer, the compression plate is in control connection with the single chip microcomputer, and the ultrasonic sensor transmits information to a corresponding computer end or mobile phone end through a GPRS device and is used for informing workers of garbage recovery;
the touch device comprises a pyroelectric sensor, the pyroelectric sensor is arranged in a first installation chamber, and the pyroelectric sensor is in information transmission connection with the single chip microcomputer;
the lower barrel body is divided into four independent barrel bodies uniformly around the long shaft, and the four independent barrel bodies are connected with the long shaft through connecting rods respectively.
2. The photoelectric sensing garbage can based on deep learning of claim 1, characterized in that, the lateral wall of every independent can body in the lower can body all is equipped with a clearance door and a button, be equipped with the handle on the clearance door, the major axis bottom is connected with electric putter's stiff end, electric putter and button control connection, electric putter's flexible end is used for directly promoting the rubbish after the compression in the independent can body.
3. The photoelectric sensing trash can based on deep learning of claim 1 or 2, wherein a light intensity sensor and a background light are further arranged at the top end of the interior of the upper can cover and located above the recognition platform, the light intensity sensor is in information transmission connection with a single chip microcomputer, and the single chip microcomputer is in control connection with the background light.
4. The photoelectric sensing trash can based on deep learning of claim 3, wherein an LED lamp is further arranged on the outer side wall of the lower can body and is in control connection with the single chip microcomputer.
5. The deep learning-based photoelectric sensing trash can of claim 1 or 3, wherein the cross section of the compression plate is one quarter of the cross section of the lower can body.
6. The photoelectric sensing trash can based on deep learning of claim 1, wherein the single chip microcomputer identifies images acquired by a camera by operating a deep learning module, the deep learning module comprises a data set and a convolutional neural network, the data set comprises an open source garbage image data set or a garbage image data set acquired by a user, and the images are preprocessed before training to enable parameters of the images to meet requirements of the convolutional neural network; the convolutional neural network is a feedforward neural network which comprises convolutional calculation and has a deep structure, an activation function and a pooling layer are arranged behind a convolutional layer, and the output of a final full-connection layer is classified by using Softmax.
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