集成电路与嵌入式系统2024，Vol.24Issue(9)：49-55,7.DOI:10.20193/j.ices2097-4191.2024.0018
基于深度学习与视觉算法的垃圾分类装置设计
Design of garbage classification device based on deep learning and visual algorithm
关源 1李博岩 1马睿1
作者信息
- 1. 吉林大学仪器科学与电气工程学院,长春 130026
- 折叠
摘要
Abstract
In order to cope with the increasingly severe challenge of waste classification,this paper designs a waste sorting device based on machine learning and vision algorithms.The device collects data from the video camera and sorts them by the Maix bit core board,in which the machine learning network MobileNet and the normalization algorithm in the vision module are applied to increase the sorting accuracy dramatically.After classification,the Maix bit core board communicates with the STM32F103 board to control the corresponding servo and the speech announce system to implement the sorting operation for the relevant type of waste.After experimental validation,this device can achieve garbage sorting speeds of less than 1 second and accuracy greater than 98％.Therefore,it possesses strong prac-ticality in real applications.关键词
垃圾分类/MobileNet/视觉算法/归一化算法/STM32F103Key words
garbage classification/MobileNet/visual algorithms/normalization algorithm/STM32F103分类
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关源,李博岩,马睿..基于深度学习与视觉算法的垃圾分类装置设计[J].集成电路与嵌入式系统,2024,24(9):49-55,7.
