实验科学与技术2026，Vol.24Issue(3)：20-26,95,8.DOI:10.12179/1672-4550.20250272
基于SE-ResNet的智能分类可压缩垃圾桶设计
Design of Intelligent Classification Compressible Garbage Bin Based on SE-ResNet
摘要
Abstract
To achieve intelligent,efficient,and accurate sorting of household waste,this study proposes an intelligent waste classification device based on an improved ResNet deep neural network integrated with a Squeeze-and-Excitation(SE)attention mechanism,designed to perform waste classification tasks.The dimensions of the sorting mechanism are optimized using the closed-loop vector method,while the servo output power is calculated based on the principle of virtual work,providing a theoretical basis for servo selection.A channel attention mechanism is integrated into the network model to facilitate effective interaction and selection among channels,thereby fully utilizing channel information.The control system employs an Arduino Mega 2 560 for sensor signal acquisition and communicates with an NVIDIA Jetson NX via a serial port.Combined with a waste-dropping motion control algorithm,this enables precise control of the motors and servos.Additionally,the system is equipped with a custom-designed compression device that mechanically squeezes compressible waste to improve space utilization.To validate its classification accuracy,twelve common types of household waste were used to test the intelligent classification garbage bins.Experimental results demonstrate that the average waste recognition accuracy of the SE-ResNet-based deep neural network reaches as high as 95.83%,meeting practical application requirements.关键词
垃圾分类/压缩功能/深度视觉/人工智能Key words
waste classification/compression function/deep vision/artificial intelligence分类
信息技术与安全科学引用本文复制引用
赵鹏飞,张效泽,马东轲,黄泽漪,闫建维..基于SE-ResNet的智能分类可压缩垃圾桶设计[J].实验科学与技术,2026,24(3):20-26,95,8.基金项目
2023年天津市普通高等学校本科教学改革与质量建设研究计划项目(A231005601) （A231005601）
天津大学本科教育教学重点建设项目(YC202201) （YC202201）
2018-2022年天津大学本科实践教学优秀示范案例(2023-2-15). （2023-2-15）
