卷积神经网络
计算机科学
分类
垃圾
人工智能
可扩展性
卷积(计算机科学)
特征(语言学)
深度学习
弹性(材料科学)
分布式计算
机器学习
人工神经网络
数据库
算法
哲学
程序设计语言
物理
热力学
语言学
作者
Mingrui Fan,Kuangji Zuo,Jingqian Wang,Jiang Zhu
出处
期刊:Systems and soft computing
[Elsevier]
日期:2023-12-01
卷期号:5: 200059-200059
被引量:1
标识
DOI:10.1016/j.sasc.2023.200059
摘要
Waste sorting plays a vital role in establishing a sustainable society by effectively reducing resource waste and promoting its recycling. However, traditional garbage sorting heavily relies on manual labor, which is inefficient, costly, and constrained by limited human resources. To address these challenges, this paper employs the convolutional neural network technique in deep learning for intelligent waste sorting. Firstly, a multi-scale processing strategy is introduced to enhance the system's resilience and accuracy by considering feature information at various scales. Secondly, a lightweight approach using tiny convolutions instead of large convolutions is adopted to reduce model parameters. Combining the advantages of both, we constructed a lightweight multiscale convolution (LMConv) and experiments the Lightweight Multiscale Convolutional Neural Network (LMNet) based on LMConv, and its optimal convolutional architecture is determined through ablation experiments. The experiment results demonstrate that LMNet outperforms other well-known convolutional neural network models in the area of garbage sorting.
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