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New Benchmark for Household Garbage Image Recognition

查看全文 作　　者：Zhize [1]Wu;Huanyi [2]Li;Xiaofeng [1]Wang;Zijun [1]Wu;Le [1]Zou;Lixiang [1]Xu;Ming [1]Tan 高影响力作者 机构地区：[1]School of Artificial Intelligence and Big Data,Hefei University,Hefei 230601,China;[2]School of Energy Materials and Chemical Engineering,Hefei University,Hefei 230601,China高影响力机构 出　　处：《Tsinghua Science and Technology》索引2022年第27卷第5期,共11页高影响力期刊 基　　金：supported by the National Natural Science Foundation of China (Nos. 12001523,11971046,12131003,and 11871081);the Scientific Research Project of Beijing Municipal Education Commission (No. KM201910005012);Beijing Natural Science Foundation Project (No. Z200002)。 摘　　要：Household garbage images are usually faced with complex backgrounds,variable illuminations,diverse angles,and changeable shapes,which bring a great difficulty in garbage image classification.Due to the ability to discover problem-specific features,deep learning and especially convolutional neural networks(CNNs) have been successfully and widely used for image representation learning.However,available and stable household garbage datasets are insufficient,which seriously limits the development of research and application.Besides,the state-of-the-art in the field of garbage image classification is not entirely clear.To solve this problem,in this study,we built a new open benchmark dataset for household garbage image classification by simulating different lightings,backgrounds,angles,and shapes.This dataset is named 30 classes of household garbage images(HGI-30),which contains 18 000 images of 30 household garbage classes.The publicly available HGI-30 dataset allows researchers to develop accurate and robust methods for household garbage recognition.We also conducted experiments and performance analyses of the state-of-the-art deep CNN methods on HGI-30,which serves as baseline results on this benchmark. 关 键 词：BENCHMARK household garbage image classification deep convolutional neural networks
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