Audio-visual combination based intelligent garbage classification-recovery method and terminal

Abstract

The invention discloses an audio-visual combination based intelligent garbage classification-recovery method and terminal. Image data of complete images of garbage of different materials is collected,and a preprocessing convolutional neural network as well as a first sub-classification model is established and trained; sound signals of impact sounds of garbage of different materials are collected, and a second sub-classification model is established and drained; garbage data is input to the preprocessing convolutional neural network to determine whether the garbage belongs to single-type garbage or mixed-type garbage; and he image and sound data is input to the first and second sub-classification models respectively for processing to obtain a garbage type result of the two models, and a final result is obtained by weighting. The intelligent garbage classification terminal comprises a garbage can body, an automatic sensor, a classification mechanism, a garbage storage box and a controldisplay. According to the invention, digital management and control of the whole garbage treatment production process can be formed, and the garbage treatment efficiency is improved.

Description

technical field [0001] The present invention relates to a garbage sorting and processing method and a terminal, in particular to an audio-visual-based intelligent garbage sorting and recycling method and a terminal. Background technique [0002] With the explosive growth of global garbage production year after year, garbage recycling has become an extremely important part of municipal solid waste management. There are many problems in relying on residents to consciously sort garbage when throwing out garbage, such as high education costs, difficult supervision, and difficulty in ensuring the accuracy of classification, etc., which restrict the implementation of more refined garbage classification schemes. Existing intelligent garbage classification methods are usually based on a single signal such as vision, which requires extremely high sample size and cannot meet the needs of real scenarios. In response to these problems, the present invention explores a new intelligent g...

AI Technical Summary

Problems solved by technology

There are many problems in relying on residents to consciously sort garbage when throwing out garbage, such as high education costs, difficult supervision, and difficult to guarantee the accuracy of classification, etc., which restrict the implementation of more refined garbage classification schemes

Existing intelligent garbage classification methods are usually based on a single signal such as vision, which requires extremely high sample size and is difficult to meet the needs of real scenarios

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