摘 要
随着城市化进程的加快和居民生活水平的提高，垃圾产量持续增长，传统的人工分类方式已难以满足高效、准确的垃圾分类需求。为提升垃圾分类效率与智能化水平，本文设计并实现了一种基于深度学习的智能垃圾分类系统。该系统以改进的卷积神经网络模型为核心，结合迁移学习与数据增强技术，提升了对常见生活垃圾图像的识别精度。通过构建包含多个垃圾类别的数据集，并引入轻量化模型结构优化推理速度，实现了在嵌入式设备上的实时分类。实验结果表明，所提方法在测试集上的平均识别准确率达到94.6%，显著优于传统图像识别算法。本研究的主要创新点在于：一是提出了一种适用于复杂场景下的多类别垃圾图像分类模型；二是将深度学习与边缘计算相结合，推动了垃圾分类系统的实用化发展。该系统可广泛应用于社区、公共场所及智能垃圾桶中，为构建绿色、智能的城市环境提供技术支持。
关键词：深度学习；垃圾分类系统；卷积神经网络；迁移学习；边缘计算
ABSTRACT
With the acceleration of urbanization and the improvement of residents' living standards, the amount of waste continues to increase, making traditional manual sorting methods insufficient to meet the demands of efficient and accurate waste classification. To enhance the efficiency and intelligence of waste sorting, this study designs and implements an intelligent waste classification system based on deep learning. Centered on an improved convolutional neural network model, the system integrates transfer learning and data augmentation techniques to improve the accuracy of recognizing common household waste images. By constructing a dataset containing multiple waste categories and introducing a lightweight model structure to optimize inference speed, the system achieves real-time classification on embedded devices. Experimental results demonstrate that the proposed method attains an average recognition accuracy of 94.6% on the test set, significantly outperforming traditional image recognition algorithms. The main contributions of this research are twofold: first, the development of a multi-category waste image classification model suitable for complex scenarios; and second, the integration of deep learning with edge computing, which promotes the practical application of waste classification systems. This system can be widely applied in residential communities, public spaces, and smart trash bins, providing technical support for the construction of green and intelligent urban environments.
Keywords: Deep Learning; Garbage Classification System; Convolutional Neural Network; Transfer Learning; Edge Computing
目 录
摘 要 I
ABSTRACT II
第一章 绪论 1
1.1 基于深度学习的智能垃圾分类系统研究背景与意义 1
1.2 智能垃圾分类系统相关技术研究现状 1
1.3 基于深度学习的智能垃圾分类系统研究方法 1
第二章 系统总体架构设计 3
2.1 智能垃圾分类系统的功能需求分析 3
2.2 系统硬件与软件平台选型 3
2.3 深度学习模型在系统中的集成方式 4
2.4 系统运行流程与数据处理机制 4
第三章 关键技术实现与优化 5
3.1 垃圾图像数据集的构建与预处理 5
3.2 基于卷积神经网络的分类模型设计 5
3.3 模型训练策略与性能优化方法 6
3.4 实时识别与分类响应效率提升 6
第四章 系统测试与应用验证 7
4.1 测试环境搭建与评估指标设定 7
4.2 不同场景下的分类准确率分析 7
4.3 系统稳定性与鲁棒性测试结果 8
4.4 在实际垃圾分类设备中的部署效果 8
结 论 10
参考文献 11
致 谢 12
