通信与信息技术Issue(1)：62-66,5.
基于深度学习的多分类垃圾识别系统设计与实现
Design and implementation of multi-classification garbage recognition system based on deep learning
摘要
Abstract
In view of the low efficiency and inaccurate classification of multi-classification garbage detection in the current garbage detection,a garbage detection system is designed and implemented.The system is based on deep learning YOLOv11n technology com-bined with PyQt5.The system uses the YOLOv11n model and uses a large number of labeled data to carry out training.Achieved high-precision identification of glass garbage,metal garbage,plastic garbage and other garbage.This system supports picture,video and cam-era input,and can perform target detection in real time.The experimental results show that the system can run stably in different scenari-os,and the maximum detection accuracy rate can reach 94%,which effectively improves the efficiency of garbage detection and classifica-tion.Garbage classification provides efficient technical support.关键词
图像识别/深度学习/YOLOv11n/PyQt5/垃圾分类/实时检测Key words
Image recognition/Deep learning/YOLOv11n/PyQt5/Garbage classification/Real-time detection分类
信息技术与安全科学引用本文复制引用
刘权,闫金萌,喻恒,周晓钰,朱英豪..基于深度学习的多分类垃圾识别系统设计与实现[J].通信与信息技术,2026,(1):62-66,5.基金项目
平顶山学院2025年人工智能教学改革研究与实践专项项目(项目编号:RGZN202503)平顶山学院大学生创新创业训练项目(项目编号:109192024072) （项目编号:RGZN202503）
