Current Situation and Countermeasures of Household Waste Classification in Feixi County, Hefei City

Feixi County has made significant progress in promoting waste classification,such as establishing a comprehensive classification system,and effectively reducing environmental pollution and waste volume.However,with the increase in waste generation,the county faces multiple challenges especially in the disposal of kitchen waste and improvement of residents environmental awareness.To address these issues,Feixi County has implemented various measures,such as strengthening the construction of infrastructure for waste classification,improving regulations and clearly defining responsibilities,enhancing residents environmental awareness to encourage their participation in waste classification through education and promotion,and increasing supervision to ensure effective implementation of the work.It emphasizes community governance,encourage all parties to participate in it,and strengthen publicity,education and training to enhance residents participation.Feixi County has achieved positive results,but efforts are needed to further improve facilities,raise awareness,enhance supervision,and ensure the continuous effectiveness of waste classification work to promote urban green sustainable development.

Focus-RCNet:a lightweight recyclable waste classification algorithm based on focus and knowledge distillation

Waste pollution is a significant environmental problem worldwide.With the continuous improvement in the living standards of the population and increasing richness of the consumption structure,the amount of domestic waste generated has increased dramatically,and there is an urgent need for further treatment.The rapid development of artificial intelligence has provided an effective solution for automated waste classification.However,the high computational power and complexity of algorithms make convolutional neural networks unsuitable for real-time embedded applications.In this paper,we propose a lightweight network architecture called Focus-RCNet,designed with reference to the sandglass structure of MobileNetV2,which uses deeply separable convolution to extract features from images.The Focus module is introduced to the field of recyclable waste image classification to reduce the dimensionality of features while retaining relevant information.To make the model focus more on waste image features while keeping the number of parameters small,we introduce the SimAM attention mechanism.In addition,knowledge distillation was used to further compress the number of parameters in the model.By training and testing on the TrashNet dataset,the Focus-RCNet model not only achieved an accuracy of 92%but also showed high deployment mobility.

Solving the Urban Domestic Waste Classification Dilemma from a Coalitional Game Perspective

The effective classification of urban domestic waste is the key to achieve a “waste-free city” and provides an essential guarantee for resource utilization. This article takes a coalitional game perspective to study the dilemmas in urban domestic waste separation from the cooperative interaction of residents, government, and enterprises. The study finds that urban domestic waste classification in China is currently facing many problems, focusing on: 1) insufficient consensus among residents, 2) shortage of input funds, 3) corporate profitability difficulties, 4) weak policy constraints, and 5) difficulties in integrating goals. In this regard, each participating body still needs to focus on collective interests, coalitional games, break the dilemma society, and promote the long-term management of urban domestic waste.

A New Paradigm for Waste Classification Based on YOLOv5

Classification of garbage is of paramount importance prior to process them to categorise physically and this process helps to manage wastes by maintaining pollution free environment.Many systems that have capability segregate garbage are on the rise,but efficient and accurate segmentation with recognition mechanisms draw the attention of researchers.A computer vision approach for classifying garbage into respective recyclable categories could be one of the effective and economical ways of processing waste.This project mainly focused on capturing real-time images of a single piece of garbage and classifying it into three divisions:paper,or metal,or biodegradable(food)waste.Each garbage class contains around 2000 images obtained from an open-source dataset and images collected from Google and personally collected custom images.The developed intelligent models provide the effectiveness of the machine and deep learning in classification with structural and non-structural data.The model used was a Convolutional Neural Network(CNN)named YOLOv5.The project showcased vision based approach capable of maintaining an accuracy of 61%.The CNN was not trained to its maximum capacity due to the difficulty of finding optimal hyperparameters,as most of the images were gathered from Google Images.

Residents'satisfaction of Beijing new regulations for domestic waste classification based on binary logistic regression:A case study of Daxing District

On the first anniversary of the implementation of the new regulations of Beijing Municipality on the management of domestic waste,to understand residents’views on the waste classification policy,the project conducted relevant investigation of the satisfaction of residents with the domestic waste classification policy in Daxing District of Beijing,China.Based on the analysis of the survey,this study uses the binary logistic regression model to explore the residents’satisfaction with the new domestic waste classification policy in Beijing and its influencing factors.The data from 398 valid questionnaires involve the demographic characteristics of residents,residents’cognition and views on Beijing municipal solid waste classification policy,and residents’satisfaction with Beijing domestic waste classification policy.The data show that the comprehensive satisfaction level of residents with the domestic waste classification policy in Beijing is quite high,up to 84.7%.Among them,the satisfaction level of residents with the details of the classification standards,the allocation of garbage cans,the publicity and supervision of the policy,incentive measures and the implementation process and effect of the policy is very high,exceeding 80%or even more than 90%.Through binary logistic regression analysis,we come to the conclusion that six factors significantly affect residents’satisfaction with Beijing municipal solid waste classification policy,such as residents’monthly income,household daily average domestic waste production,publicity of waste classification policy,supervisors’better understanding of waste classification standards,guidance of waste delivery by community classification supervisors,and convenience of waste classification process.

Intelligent Garbage Recycling: Design and Implementation Exploration of Automatic Classification System

This paper introduces an intelligent waste recycling automatic classification system,which integrates sensors,image recognition,and robotic arms to achieve automatic identification and classification of waste.The system monitors the composition and properties of waste in real time through sensors,and uses image recognition technology for precise classification,and the robotic arm is responsible for grabbing and disposing.The design and implementation of the system have important practical significance and application value,and help promote the popularization and standardization of waste classification.This paper details the system s architecture,module division,sensors and recognition technology,robotic arm and grabbing technology,data processing and control system,and testing and optimization process.Experimental results show that the system has efficient waste recycling efficiency and accuracy in practical applications,bringing new development opportunities to the waste recycling industry.

Study on the relationship between waste classification,combustion condition and dioxin emission from waste incineration

Domestic waste in China is mainly collected as a combination of different types of materials.The components are variable and complex,with very different combustion characteristics making it difficult to optimize the burning to reduce pollution.There are still some controversies about the accuracy of using carbon monoxide(CO)emission to characterize waste incineration performance.Here,we investigated the relationship between waste classification,incineration conditions and dioxin emission and concluded that the concentration of CO in flue gas could not be used as the only criterion of combustion efficiency and safety.Considering the close relationship between the formation of polychlorinated dibenzo-p-dioxins and dibenzofurans(PCDD/Fs)and products of incomplete combustion,the relatively low concentrations of CO are not a reliable indicator that an incinerator equipped with an activated carbon injection system and fabric filter could achieve the national standards for PCDD/F emission.The goal,therefore,is not only to lower the emission of PCDD/Fs and other pollutants through clas-sifying the waste components at the source,but also to reduce the need for the treatment of incinerated waste to protect the environment and to increase the power generation efficiency of municipal solid waste incineration(MSWI)plants.As the demand for waste disposal continues to rapidly increase,the need for a safe waste incineration system with dioxin emission controls makes the classification of waste an indispensable part of future MSWI systems.

Long-tailed object detection of kitchen waste with class-instance balanced detector

Intelligent detection and classification of kitchen waste can promote ecological sustainability by replacing inefficient manual processes.However,the presence of non-degradable waste mixed in kitchen waste often follows a long-tailed distribution,making it challenging to train convolutional neural network-based object detectors,which results in the unsatisfactory detection of tailclass waste.To address this challenge,we propose a class-instance balanced detector(CIB-Det) for intelligent detection and classification of kitchen waste.CIB-Det implements two strategies for the loss function:the class-balanced strategy(CBS)and the instance-balanced strategy(IBS).The CBS focuses more on tail classes,and the IBS concentrates on hard-to-classify instances adaptively during training.Consequently,CIB-Det comprehensively and adaptively addresses the long-tailed issue.Our experiments on a real dataset of kitchen waste images support the effectiveness of CIB-Det for kitchen waste detection.