摘要
针对海量废弃家电回收图像数据在回收技术中难以有效利用的问题,提出了一种基于ResNet和多尺度卷积的废弃家电回收图像分类模型(Multi-scale and Efficient ResNet,ME-ResNet)。首先,基于残差结构设计了多尺度卷积模块以提升不同尺度特征信息提取能力,在此基础上基于ResNet设计了针对废弃家电回收图像分类问题的ME-ResNet模型;其次,通过用深度可分离卷积替换多尺度卷积中的部分卷积层,实现ME-ResNet模型轻量化;最后,通过与其他卷积神经网络的对比实验,对ME-ResNet及其轻量化模型的性能进行了验证。研究结果表明:相较于经典的卷积神经网络ResNet34,ME-ResNet及其轻量化模型均能有效提升识别准确度,针对构建的数据集,其最优准确率分别提升了1.2%和0.3%,宏精确率分别提升了1.7%和0.9%,宏召回率分别提升了1.3%和0.2%,宏F1分数分别提升了1.5%和0.5%。
In response to the challenge of effectively utilizing a massive amount of images in discarded household appliances recycling techniques,a discarded household appliance image recognition model,named ME-ResNet(multi-scale and efficient ResNet),is proposed based on ResNet and multi-scale convolution.Firstly,a multi-scale convolution module is designed using a residual structure to enhance the model's capability in extracting feature information across different scales.Building upon this,the ME-ResNet model is specifically designed for the classification of discarded household appliance images based on ResNet.Secondly,lightweighting of the ME-ResNet model is achieved by replacing certain convolutional layers in multi-scale convolution with depthwise separable convolution.Finally,the performance of ME-ResNet and its lightweight variant are validated through comparative experiments with other convolutional neural networks.Research results demonstrate that both ME-ResNet and its lightweight model effectively improve recognition accuracy.Compared to the classical convolutional neural network ResNet34,ME-ResNet and its lightweight version achieve respective optimal accuracy increases of 1.2% and 0.3%,macro-precision increases of 1.7% and 0.9%,macro-recall increases of 1.3%and 0.2%,and macro-F1 score increases of 1.5% and 0.5%,respectively.
作者
雷帅
仇明鑫
柳先辉
张颖瑶
LEI Shuai;QIU Mingxin;LIU Xianhui;ZHANG Yingyao(School of Electronics and Information Engineering,Tongji University,Shanghai 201804,China)
出处
《计算机科学》
北大核心
2025年第S1期377-383,共7页
Computer Science
基金
国家重点研发计划(2022YFB3305802)。
作者简介
LEI Shuai,born in 2001,postgraduate.His main research interests include industrial intelligence and big data,2331810@tongji.edu.cn;通信作者:张颖瑶,born in 1984,Ph.D,associate professor.Her main research interests include machine learning and big data.zhangyingyao@tongji.edu.cn。
