Coupling innovation method and feasibility analysis of garbage classification

TL;DR

This paper introduces a comprehensive approach combining multiple identification techniques and a coupling algorithm to improve the accuracy, intelligence, and cost-effectiveness of garbage classification systems.

Contribution

It presents novel methods for material and profile identification, and a coupling algorithm integrating these methods for enhanced garbage classification accuracy.

Findings

Improved classification accuracy through combined identification methods.

Enhanced system intelligence and cost-effectiveness.

Effective real-time measurement of garbage can space.

Abstract

In order to solve the recent defect in garbage classification - including low level of intelligence, low accuracy and high cost of equipment, this paper presents a series of methods in identification and judgment in intelligent garbage classification, including a material identification based on thermal principle and non-destructive laser irradiation, another material identification based on optical diffraction and phase analysis, a profile identification which utilizes a scenery thermal image after PCA and histogram correction, another profile identification which utilizes computer vision with innovated data sets and algorithms. Combining AHP and Bayesian formula, the paper innovates a coupling algorithm which helps to make a comprehensive judgment of the garbage sort, based on the material and profile identification. This paper also proposes a method for real-time space measurement of garbage cans, which based on the characteristics of air as fluid, and analyses the functions of air cleaning and particle disposing. Instead of the single use of garbage image recognition, this paper provides a comprehensive method to judge the garbage sort by material and profile identifications, which greatly enhancing the accuracy and intelligence in garbage classification.