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摘要

随着人类科技的进步和生活质量的提高，随之而产生的生活垃圾也

越来越多，因此如何有效的回收处理生活垃圾成为人们关注的焦点。调

查研究发现，在源头处对垃圾进行分类处理的方法是整个垃圾分类处理

流程中最高效的也是分类最彻底的。

而当前的分类规则其一是难以做到各地全部统一，另一方面是垃圾

分类种类繁多，难以区分，会对人的生活质量造成一定影响。

目前市面上出现了种类繁多的“智能垃圾桶”，但基本都只是实现了

自动开合功能，不具备垃圾分类识别的能力。而当前的人工智能技术正

在飞速发展，深度学习领域的图像识别方向有了长足的发展，使得使用

图像识别技术对垃圾进行分类成为可能。

选择正确的图像识别分类算法是本项目的重中之重。因此，本文对

过往的图像识别方面的突出贡献的算法做介绍和总结，把握其发展的脉

络，从而说明选择 MobileNetV2 网络的原因。

在本项目中，系统可分为垃圾识别模块和垃圾分类投放模块，即识

别模块和控制模块。在垃圾投入到垃圾桶中，由识别模块对垃圾进行识

别分类，再把分类结果发送给控制模块，由控制模块将其投放到对应的

垃圾桶中。经过实际测试，垃圾的类别判断正确率能在可接受范围内，

而识别分类速度则在经过分类结果滤波后，平均成功识别一次垃圾的平

均时间为 4 秒。这证明该项目在智能垃圾分类领域还是很有发展前景的。

关键词：垃圾分类,卷积神经网,MobileNetV

Abstract

With the advancement of human science and technology and the

improvement of quality of life, more and more domestic waste is generated.

Therefore, how to effectively recycle and treat domestic waste has become the

focus of people's attention. The investigation and study found that the method

of sorting waste at the source is the most efficient and the most thorough in

the entire waste sorting process.

One of the current classification rules is that it is difficult to unify all

regions, on the other hand, there are many types of garbage classification,

which are difficult to distinguish, which will have a certain impact on people's

quality of life.

At present, there are a variety of "smart trash cans" on the market, but

basically only the automatic opening and closing function is realized, and the

ability to sort and identify trash is not available. The current artificial

intelligence technology is developing rapidly, and the direction of image

recognition in the field of deep learning has made great progress, making it

possible to use image recognition technology to classify garbage.

Choosing the correct image recognition classification algorithm is the top

priority of this project. Therefore, this article introduces and summarizes the

algorithms that have made outstanding contributions in the past in image

recognition, grasping the development context, and thus explaining the

reasons for choosing the MobileNetV2 network.

In this project, the system can be divided into a garbage recognition

module and a garbage classification delivery module, namely a recognition

module and a control module. After the garbage is put into the garbage bin,

the recognition module classifies the garbage, and then sends the classification

result to the control module, and the control module puts it into the

corresponding garbage bin. After actual testing, the classification accuracy

rate of garbage can reach 90%, and the recognition and classification speed is

after filtering the classification results, and the average time for successfully

identifying a garbage is 4 seconds. This proves that the project is still very

promising in the field of intelligent waste classification.

Key words: Garbage Classification; Convolutional Neural Network;

中文摘要..........................................................................................................I

Abstract..........................................................................................................II

目录 ...............................................................................................................IV

图目录 .........................................................................................................VII

表目录........................................................................................................VIII

第一章绪论 ...................................................................................................1

1.1 课题研究背景与意义 ...........................................................................1

1.1.1 研究背景.........................................................................................1

1.1.2 意义.................................................................................................2

1.2 国内外垃圾分类及研究现状 ...............................................................3

1.2.1 国外现状.........................................................................................3

1.2.2 国内现状.........................................................................................3

1.3 本文研究内容 .......................................................................................3

第二章智能分类垃圾桶总体设计 ...............................................................5

2.1 概述 .......................................................................................................5

2.2 系统分析及设计思路 ...........................................................................5

2.3 系统框架 ...............................................................................................5

2.4 系统总体运行流程 ...............................................................................6

2.4.1 主处理器及主控制器处理流程.....................................................6

2.5 本章小结 ...............................................................................................7

第三章智能垃圾分类图像处理原理 ...........................................................8

3.1 卷积神经网络对图像的基本操作 .......................................................8

3.1.1 卷积操作.........................................................................................8

3.1.2 池化操作.........................................................................................9

3.2 LeNet 网络 ...........................................................................................10

3.2.1 综述...............................................................................................10

3.2.2 特点...............................................................................................12

3.3 AlexNet 网络........................................................................................13

3.3.1 概述...............................................................................................13

3.3.2 特点...............................................................................................13

3.4 VGGNet 网络.......................................................................................14

3.4.1 特点...............................................................................................15

3.5 GoogleNet 网络及 Inception 架构 ......................................................16

3.5.1 背景介绍.......................................................................................16

3.5.2 InceptionV1....................................................................................16

3.5.3 InceptionV2....................................................................................18

3.5.4 InceptionV3....................................................................................20

3.6 ResNet 网络 .........................................................................................21

3.6.1 综述...............................................................................................21

3.6.2 残差结构.......................................................................................22

3.7 MobileNet.............................................................................................23

3.7.1 背景介绍.......................................................................................23

3.7.2 MobileNetV1 .................................................................................24

3.7.3 MobileNetV2 .................................................................................24

3.8 本章小结 .............................................................................................27

第四章智能垃圾桶控制系统硬件设计 .....................................................28

4.1 智能垃圾桶整体结构分布 .................................................................28

4.2 STM32 硬件系统框架.........................................................................28

4.3 STM32 微处理器.................................................................................29

4.4 步进电机与步进电机驱动器 .............................................................29

4.4.1 步进电机.......................................................................................29

4.4.2 步进电机驱动器...........................................................................29

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