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基于深度残差收缩网络的校园垃圾图像分类 被引量:2

Garbage Image Classification of Campus Based on Deep Residual Shrinkage Network
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摘要 针对现实生活中垃圾分类知识普及不够,许多城市和学校都面临着垃圾分类困难的问题,利用神经网络对分类问题的高效性和准确性,通过一种基于ResNet网络和SENet网络的深度残差收缩网络实现垃圾图像分类。通过对Garbage数据集进行筛选得到实验所需数据集,并对ResNet进行改进,将SENet和软阈值化操作加入ResNet结构中。实验结果表明,该方法通过网络训练和超参数调整,得到了较好的识别率,在校园垃圾分类中获得了较好的识别效果,具有一定可行性。 There is a deficiency of information available on waste classification,and many municipalities and educational institutions struggle with this issue.We address this challenge by utilizing the efficiency and accuracy of the neural networks to classify items and implement waste image classification with a deep residual shrinkage network built on the ResNet network and SENet network.By filtering the Garbage dataset to obtain the data set necessary for the experiment,and by enhancing ResNet,SENet and soft threshold processes are incorporated into the ResNet structure.And by training the network and optimizing its hyperparameters,a greater recognition rate and recognition effect are achieved for the classification of campus waste.The experimental findings indicate that the proposed approach is feasible to a certain extent.
作者 王玉 张燕红 周昱洲 林鸿斌 WANG Yu;ZHANG Yanhong;ZHOU Yuzhou;LIN Hongbin(College of Computer Science and Technology,Jilin University,Changchun 130012,China;College of Software,Jilin University,Changchun 130012,China)
出处 《吉林大学学报(信息科学版)》 CAS 2023年第1期186-192,共7页 Journal of Jilin University(Information Science Edition)
基金 吉林大学创新实验基金资助项目(202110183X416)。
关键词 深度学习 残差网络 注意力机制 图像分类 deep learning residual network attention mechanism image classification
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