CN116620744A - Intelligent classification system based on deep learning and implementation method thereof - Google Patents
Intelligent classification system based on deep learning and implementation method thereof Download PDFInfo
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Abstract
The invention relates to the technical field of computer vision, in particular to an intelligent classification system based on deep learning and an implementation method thereof, which comprises the following steps: step 1: determining the class of garbage classification and making a data set; step 2: designing a hardware system; step 3: writing a software program; step 4: manufacturing a garbage classification model; step 5: quantification and deployment; step 6: testing and debugging; step 7: mounting and using; the beneficial effects are as follows: according to the intelligent classification system based on deep learning and the implementation method thereof, the light sensor detects that garbage enters the movable buffer box and sends signals, the camera receives the signals to capture garbage images, the image processing is carried out, the model classifies garbage and sends the signals, and the buffer box receives the signals to release the garbage corresponding to the garbage classification box when the garbage moves. The invention provides an effective method for classifying garbage, which promotes sustainable garbage management practice and has positive significance for environmental protection, resource recovery and urban management efficiency improvement.
Description
Technical Field
The invention relates to the technical field of computer vision, in particular to an intelligent classification system based on deep learning and an implementation method thereof.
Background
The current global urban process is accelerated, the garbage generation amount shows a trend of rapid increase, and garbage classification has become an important task of the current society. The correct garbage classification method involves separating biodegradable and non-degradable garbage and separating recyclable garbage from other garbage. Biodegradable waste includes food residues, paper and flowers, and non-degradable waste includes plastics, rubber and glass. Recyclable waste includes metal, plastic, glass, paper, and the like. Through correctly classifying the garbage, the pollution of the garbage to the environment can be reduced, the waste of natural resources is reduced, the recycling of the resources can be promoted, the quantity of the wastes is reduced, and the garbage disposal cost is reduced.
In the prior art, the garbage recycling efficiency can be improved by 95% by manually classifying the garbage before recycling the garbage. However, sorting large amounts of waste takes a long time, potentially exposing people to contaminated waste and hazardous materials. Automatic classification is another technique for garbage classification. There are many ways to automatically sort the waste. Classification using images is one of the most effective methods of classifying objects or garbage.
However, in the conventional visual method, a large amount of data is learned from a huge image library by using a large Convolutional Neural Network (CNN) model and transfer learning, so that high classification accuracy is obtained, but the model parameters are too large, and the real-time problem in classification is not considered.
Disclosure of Invention
The invention aims to provide an intelligent classification system based on deep learning and an implementation method thereof, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: an intelligent classification system based on deep learning, comprising hardware and a software algorithm, the hardware comprising:
the garbage receiver is used for receiving garbage and placing the garbage aside for shooting before classification;
a movable dustbin for moving the garbage received from the receiver bin to a corresponding dustbin position and placing the garbage into the dustbin;
the garbage bin is used for sorting garbage, and four different types of garbage are respectively placed into the corresponding garbage bin;
a programmable logic controller for controlling the machine;
RGB camera: the garbage image shooting device is used for shooting garbage images entering the receiver box and is arranged at the top of the receiver box;
light curtain sensor: comprising a transmitter and a receiver for detecting an object between the receiver and the transmitter;
and (3) a computer: the garbage classification software is used for running garbage classification software and simultaneously communicating with the Arduino Uno so as to control other hardware of the garbage classifier;
the software algorithm is the software of the intelligent garbage classifier written in the Python programming language, and comprises the following components:
the Pyfirata library: the intelligent garbage sorting machine is used for connecting a computer with hardware in the intelligent garbage sorting machine.
PyTorch library: a machine learning framework for image classification.
An intelligent classification implementation method based on deep learning is characterized by comprising the following steps: the intelligent classification implementation method comprises the following steps:
step 1: determining the class of garbage classification and making a data set;
step 2: designing a hardware system;
step 3: writing a software program;
step 4: manufacturing a garbage classification model;
step 5: quantification and deployment;
step 6: testing and debugging;
step 7: mounting and using.
Preferably, when determining the classification of garbage and making a data set, classifying garbage into different classes, such as a cardboard box or paper, a plastic bottle, a pop-top can and a food bag, according to a network picture or a picture taken by the user; all images will be divided into two groups at a ratio of 8:2, 80% of the images will be used to train the model, and 20% of the images will be used to test the model.
Preferably, when the software program is written, the software program is written by using Python, including using a PyTorch library for image classification, communicating with Arduino Uno, and controlling the PLC.
Preferably, when the garbage classification model is made, a model based on a depth separable convolution network is trained by using the image dataset, and training is performed.
Preferably, upon quantification and deployment, the trained model will be quantified and deployed into the garbage classifier's software program.
Preferably, during testing and debugging, the performance of the garbage classifier is tested and debugged to ensure that the garbage classifier can accurately classify garbage into a correct garbage can.
Preferably, when the garbage classifier is installed and used, the garbage classifier is installed at a required position, and a user is provided with a use instruction, so that the garbage classifier can be used correctly.
Compared with the prior art, the invention has the beneficial effects that:
according to the intelligent classification system based on deep learning and the implementation method thereof, the light sensor detects that garbage enters the movable buffer box and sends signals, the camera receives the signals to capture garbage images, the image processing is carried out, the model classifies garbage and sends the signals, and the buffer box receives the signals to release the garbage corresponding to the garbage classification box when the garbage moves. The invention provides an effective method for classifying garbage, which promotes sustainable garbage management practice and has positive significance for environmental protection, resource recovery and urban management efficiency improvement.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions, and advantages of the present invention more apparent, the embodiments of the present invention will be further described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are some, but not all, embodiments of the present invention, are intended to be illustrative only and not limiting of the embodiments of the present invention, and that all other embodiments obtained by persons of ordinary skill in the art without making any inventive effort are within the scope of the present invention.
Example 1
The invention provides a technical scheme that: an intelligent garbage classification system based on deep learning comprises hardware and software algorithms. The hardware includes:
1 garbage receiver: made of aluminum and sized 18x 35x 25 cm for receiving the trash and placing it aside for shooting before sorting. The top of the receptacle housing contains a bin for holding the waste. The bottom of the receptacle box can be opened and closed to release the waste into the movable waste bin below. The garbage receptacle bottom is white painted because the white background allows for more efficient image classification.
2 removable dustbin: made of aluminum, similar to the receiver tank. It moves the refuse received from the receiver bin to the corresponding refuse bin position and places the refuse into the refuse bin.
3, a garbage can: made of acrylic, with a size of 25x 59x 37 cm. After the garbage classification, four different types of garbage are respectively put into corresponding garbage cans. The dustbin can be cleaned and treated easily.
4 Programmable Logic Controller (PLC): for controlling the machine. A PLC in an intelligent garbage sorting machine controls the opening and closing of the garbage receiver and movable garbage bin, and moves along a track to place garbage into the appropriate container.
5RGB camera: for taking images of the waste entering the receiver tank, mounted on top of the receiver tank.
6, light curtain sensor: including a transmitter and a receiver that generate a plurality of infrared beams. They are used to detect objects between the receiver and the transmitter. The sensor is mounted at the inlet of the receptacle housing for detecting any refuse entering the receptacle housing. If debris is detected, the sensor sends a signal telling the machine to begin capturing images and running the process.
And 7, a computer: for running the garbage classification software while communicating with the Arduino Uno to control other hardware of the garbage classifier.
The system software is software of an intelligent garbage classifier written in a Python programming language, and comprises:
1 Pyfirata Bank: the intelligent garbage sorting machine is used for connecting a computer with hardware in the intelligent garbage sorting machine.
2PyTorch library: a machine learning framework for image classification.
Example two
On the basis of the first embodiment, an intelligent classification implementation method based on deep learning is provided, which is characterized in that: the intelligent classification implementation method comprises the following steps:
step 1: the class of garbage classification is determined and a dataset is created. Garbage is classified into various categories such as cardboard boxes or papers, plastic bottles, pop cans, and food bags according to available network pictures or pictures taken by themselves. Then, all images will be divided into two groups in a 8:2 ratio. 80% of the images will be used to train the model and 20% of the images will be used to test the model.
Step 2: the hardware system is designed. The hardware system comprises a garbage receiver, a movable garbage can, a Programmable Logic Controller (PLC), an RGB camera, a light curtain sensor and the like. The PLC is responsible for controlling opening and closing of the garbage sorting machine, moving tracks and the like, the RGB camera is used for shooting garbage images, and the light curtain sensor is used for detecting whether garbage enters the garbage can.
Step 3: a software program is written. Software programs were written using Python, including using the PyTorch library for image classification, communicating with Arduino Uno, controlling PLC, etc.
Step 4: and manufacturing a garbage classification model. A model based on a depth separable convolutional network is trained using the image dataset and is trained.
Step 5: quantification and deployment. The trained model will be quantized and deployed into the garbage classifier's software program.
Step 6: testing and debugging. The performance of the garbage classifier is tested and debugged to ensure that it can accurately classify garbage into the correct garbage bin.
Step 7: mounting and using. The garbage classifier is installed at a desired location and instructions are provided to the user so that the garbage classifier can be used properly.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. An intelligent classification system based on deep learning comprises hardware and software algorithms, and is characterized in that: the hardware includes:
the garbage receiver is used for receiving garbage and placing the garbage aside for shooting before classification;
a movable dustbin for moving the garbage received from the receiver bin to a corresponding dustbin position and placing the garbage into the dustbin;
the garbage bin is used for sorting garbage, and four different types of garbage are respectively placed into the corresponding garbage bin;
a programmable logic controller for controlling the machine;
RGB camera: the garbage image shooting device is used for shooting garbage images entering the receiver box and is arranged at the top of the receiver box;
light curtain sensor: comprising a transmitter and a receiver for detecting an object between the receiver and the transmitter;
and (3) a computer: the garbage classification software is used for running garbage classification software and simultaneously communicating with the Arduino Uno so as to control other hardware of the garbage classifier;
the software algorithm is the software of the intelligent garbage classifier written in the Python programming language, and comprises the following components:
the Pyfirata library: the intelligent garbage sorting machine is used for connecting a computer with hardware in the intelligent garbage sorting machine;
PyTorch library: a machine learning framework for image classification.
2. A method for implementing deep learning-based intelligent classification for a deep learning-based intelligent classification system in accordance with claim 1, wherein: the intelligent classification implementation method comprises the following steps:
step 1: determining the class of garbage classification and making a data set;
step 2: designing a hardware system;
step 3: writing a software program;
step 4: manufacturing a garbage classification model;
step 5: quantification and deployment;
step 6: testing and debugging;
step 7: mounting and using.
3. The intelligent classification implementing method based on deep learning as claimed in claim 2, wherein: when determining the classification of garbage and making a data set, classifying garbage into different classes, such as a cardboard box or paper, a plastic bottle, a pop can and a food bag, according to network pictures or pictures taken by the user; all images will be divided into two groups at a ratio of 8:2, 80% of the images will be used to train the model, and 20% of the images will be used to test the model.
4. The intelligent classification implementing method based on deep learning as claimed in claim 2, wherein: when the software program is written, the Python is used for writing the software program, and the PyTorch library is used for image classification, communication with Arduino Uno and control of the PLC.
5. The intelligent classification implementing method based on deep learning as claimed in claim 2, wherein: when the garbage classification model is manufactured, the image dataset is used for training a model based on a depth separable convolution network, and training is carried out.
6. The intelligent classification implementing method based on deep learning as claimed in claim 2, wherein: upon quantification and deployment, the trained model will be quantified and deployed into the garbage classifier's software program.
7. The intelligent classification implementing method based on deep learning as claimed in claim 2, wherein: during testing and debugging, the performance of the garbage classifier is tested and debugged to ensure that the garbage classifier can accurately classify garbage into a correct garbage can.
8. The intelligent classification implementing method based on deep learning as claimed in claim 2, wherein: when the garbage classifier is installed and used, the garbage classifier is installed at a required position, and a use instruction is provided for a user, so that the garbage classifier is correctly used.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118133027A (en) * | 2024-05-07 | 2024-06-04 | 葛洲坝集团生态环保有限公司 | Solid waste separation auxiliary method and system based on deep learning |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118133027A (en) * | 2024-05-07 | 2024-06-04 | 葛洲坝集团生态环保有限公司 | Solid waste separation auxiliary method and system based on deep learning |
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