基于机器视觉的垃圾分类系统设计

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关键词：YOL0V5模型；垃圾分类；深度学习

doi：10.3969/J.ISSN.1672-7274.2025.05.022

中图分类号：TP31 文献标志码：A 文章编码：1672-7274（2025）05-0064-03

Abstract： This paper realizes automatic identification of garbage images through the YOLOV5s model， introduces the CBAM attention mechanism to optimize the performance of the YOLOV5s model in practical applications.By equipping an STM32 microcontroller as the main control device，the stepper motor is controlled to sort garbage based on the clasificationresults oftheYOLOV5s model.TVOCand infraredsensing modules are integrated to monitor the internalenvironmentof thetrash can inreal-time.This design applies deep learning modelsand embeddeddevices to garbage classification， which holds significant practical value for waste sorting.

Keywords： YOLOV5s model; waste sorting; deep learning

0 引言

随着经济的发展，生活垃圾运量呈现上升趋势，截至2022年，我国生活垃圾运量高达24444.7万吨，垃圾分类平均覆盖率达到 82.5% ，但目前城市的生活垃圾分类存在分类效率低下，人力耗费较高，处理环境恶劣等问题。（剩余3628字）