Household garbage classification method based on deep learning
A technology of household garbage and classification methods, which is applied to biological neural network models, instruments, character and pattern recognition, etc., can solve the problems of lack of research on household garbage image classification algorithms, and achieve the effect of reducing the amount of parameters
Active Publication Date: 2021-02-12
FUZHOU UNIV +1
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However, there is a lack of research on classification algorithms for household waste images
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[0025] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.
[0026] It should be pointed out that the following detailed description is exemplary and is intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
[0027] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combina...
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The invention relates to a household garbage classification method based on deep learning. The method comprises the steps of establishing a household garbage data set, and expanding the data set by adopting a data enhancement method; establishing a neural network classification model, and replacing the common convolution of the ResNet18 residual unit with a phantom module to obtain a G-ResNet18 network; preprocessing the expanded data set, and inputting the preprocessed data set into a G-ResNet18 network for classification training; preprocessing the household garbage pictures to be classified, inputting the preprocessed household garbage pictures to the trained G-ResNet18 model, and outputting a classification result. Experimental results show that the recognition precision of the G-ResNet18 network on the experimental data set reaches 91.6%, the recognition precision is improved by 1%, and the parameter quantity of the network is reduced by 46%. The method can greatly reduce the parameter quantity of the network while not reducing the network recognition precision, and can be applied to the intelligent classification of garbage.
Description
technical field [0001] The invention relates to the field of research on the application of deep learning image classification algorithms, in particular to a deep learning-based domestic waste classification method. Background technique [0002] According to the China Urban and Rural Construction Statistical Yearbook, the generation of municipal solid waste in my country increased from 25 million tons in 1979 to 228 million tons in 2018. With the improvement of people's living standards, the amount of garbage generated is still on the rise. Effective recycling of domestic waste has become an urgent problem, which is of great significance to sustainable development. Garbage sorting is a prerequisite for recycling. At present, my country's waste classification is mainly manual sorting, which has the disadvantages of high labor intensity and low efficiency. It is of great significance to realize the intelligence and automation of garbage sorting. Garbage image classificatio...
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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/214G06F18/24Y02W30/10
Inventor 林志贤郑佑顺郭太良周雄图张永爱林珊玲
Owner FUZHOU UNIV