| 垃圾图像识别研究进展 |
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| 引用本文： | 金佩薇,姚燕,梁晓瑜,蔡晋辉. 垃圾图像识别研究进展[J]. 环境工程, 2022, 40(1): 196-206. DOI: 10.13205/j.hjgc.202201029 |
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| 作者姓名： | 金佩薇 姚燕 梁晓瑜 蔡晋辉 |
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| 作者单位： | 中国计量大学计量测试工程学院,杭州310018 |
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| 摘 要： | 实现生活垃圾自动分类是解决城市固体废弃物(municipal solid waste,MSW)问题的有效途径.着眼于近10年基于计算机视觉的垃圾图像识别相关研究,依据垃圾自动分类方法的差异性,将当前现有相关研究分为基于传统机器学习方法和基于深度学习方法.介绍了机器学习方法以及深度学习方法特征提取方式,对比分析了传统机器...
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| 关 键 词： | 图像识别 深度学习 特征提取 卷积神经网络 MSW |
| 收稿时间： | 2021-01-21 |
OVERVIEW OF RESEARCHES ON MUNICIPAL SOLID WASTE IMAGE RECOGNITION |
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| Affiliation: | College of Metrology & Measurement Engineering, China Jiliang University, Hangzhou 310018, China |
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| Abstract: | Realizing the automatic classification of domestic waste is an effective way to solve the increasing problems on municipal solid waste(MSW). The thesis focused on the researches on waste image recognition based on computer vision in the past ten years. According to the differences of automatic waste classification methods, the current existing related research was divided into traditional machine learning methods and deep learning methods. It illustrated the machine learning method and the feature extraction method of the deep learning method, compared and analyzed the advantages and disadvantages of the traditional machine learning method and the waste type recognition based on the deep learning method, focused on the application research of the general neural network of the deep learning method. In addition, the data sets used in the current research on waste image recognition were introduced, and the problem of current waste image recognition were analyzed and prospected finally. |
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