Garbage Classification Based on ShuffleNet and Edge Detection Preprocessing

Municipal solid waste management in China: using commercial management to solve a growing problem

In order to solve the problem of municipal solid waste and improve the recycling rate of resources, an intelligent garbage classification system is designed. The reader can identify the electronic tag on the garbage bag, automatically judge and open the corresponding garbage bin cover, which can be recycled or non-recyclable, so as to achieve the effect of garbage classification. At the same time, through the built-in distance, temperature and humidity sensors in the dustbin, real-time monitoring of whether the garbage is full, whether there is fire, etc., and the detected information is transmitted to the background through the network, and the alarm will be given in case of abnormality. It can reduce the workload of property cleaning staff and prevent the occurrence of fire and the spread of bacteria.

Benefits analysis of classification of municipal solid waste based on system dynamics

In order to solve the problem of municipal solid waste in developed countries, it is common to incinerate it in incinerators. Therefore, the definition of regression dependence, which describes the dynamics of the prevalence of municipal solid waste incineration methods in Ukraine to solve the problem of municipal solid waste management, is an urgent scientific and technical task. The aim of the study is to determine, by regression analysis, the dependences that describe the dynamics of the prevalence of municipal solid waste incineration methods in Ukraine to solve the problem of municipal solid waste management. The method of regression analysis of the results of one-factor experiments and other pair dependences with the choice of a more adequate type of function from the 16 most common variants by the criterion of the maximum correlation coefficient was used during the research. The regression was performed on the basis of linearizing transformations, which allow reducing the nonlinear dependence to the linear one. The coefficients of the regression equation were determined by the method of least squares with the help of the developed computer program "RegAnaliz", which is protected by the copyright registration certificate for the work. Adequate regression dependences describing the dynamics of the prevalence of municipal solid waste incineration methods in Ukraine are obtained. A graphical dependence is constructed, which describes the dynamics of the prevalence of municipal solid waste incineration methods in Ukraine and allows illustrating this dynamics, to show a sufficient convergence of theoretical and actual results. It is established that in Ukraine during 2012—2019 the prevalence of municipal solid waste incineration for energy purposes increased exponentially, and the prevalence of non-municipal solid waste incineration - decreased exponentially hyperbolic. It is projected that in Ukraine in 2031 among the methods of solid waste incineration, the prevalence of incineration for energy under appropriate conditions may be 99,66 %, and without energy — 0,34 %.

The problem of municipal solid waste (MSW) has been a serious threat to the health of residents and the ecological environment of city. Waste classification can maximize waste resource utilization, reduce the amount of garbage disposal, improve the quality of the living environment, and is the fundamental solution to the problem of "garbage siege" phenomenon. However, over the years, our country MSW classification recycling work remains stagnant. It is proposed that the classification recycling of MSW in China can choose the clearest, simplest and most harmful areas, start from the household appliances, electronic waste, vegetable waste, packaging and kitchen waste recycling, to recycle social resources effectively and realize the sustainable development of city.

Garbage classification system based on improved ShuffleNet v2

The aim of the article is to predict economic and social consequences of possible solutions to the problem of municipal solid waste (MSW) recycling in Irkutsk region, describe technologies aimed at reducing cost and optimizing business processes of Irkutsk company “Spetsautokhozystvo” which is the most typical enterprise dealing with MSW recycling. The authors used the methods of system, integrated, functional, statistical, structural and comparative analyses, expert and statistical forecasts. The article says that Irkutsk region was divided into two zones - southern and northern ones. The article identified standards of MSW production per resident depending on settlements and MSW transportation and recycling fares. Irkutsk region is not ready to implement a large-scale MSW recycling project. The solutions to the MSW recycling for Irkutsk and Irkutsk region, implementation of technologies aimed at reducing costs and optimizing business processes of “Spetsautokhozystvo” were described. The need for developing a target MSW recycling project in Irkutsk and Irkutsk region was substantiated.

The dense population and the large amount of domestic waste generated make it difficult to determine the best route and departure time for waste removal trucks in a city. Aiming at the problems of municipal solid waste (MSW) removal and transportation not in time, high collection and transportation costs and high carbon emissions, this paper studies the vehicle routing problem of municipal solid waste removal under the influence of time-dependent travel time, traffic congestion and carbon emissions. In this paper, a dual objective model with the lowest total economic cost and the highest garbage removal efficiency is established, and a DCD-DE-NSGAII algorithm based on Dynamic Crowding Distance and Differential Evolution is designed to improve the search ability, improve the convergence speed and increase the diversity of the optimal solution set. The results show that: according to the actual situation of garbage collection and transportation, the method can scientifically plan the garbage collection and transportation route, give a reasonable garbage collection scheme and departure time, and effectively avoid traffic congestion time; Through algorithm comparison, the algorithm and model proposed in this paper can reduce collection and transportation costs, improve transportation efficiency and reduce environmental pollution.

Garbage classification is an essential work in daily life. With the development of artificial intelligence (AI), we have begun to use object detection to achieve garbage classification. However, the stacking and occlusion of garbage often make the detection performance unsatisfactory. In this paper, YOLOv5 algorithm is used for garbage detection and classification. YOLOv5 has three detection heads, which can detect objects at multiple scales. At the same time, Mosaic and Coarse Dropout image augmentation techniques are used to process our private data set of garbage, which improves the diversity of images. In this way, the target classification accuracy of the detection model can reach 75.7%, which is significantly improved compared with 69.9% without data augmentation. This paper provides a new technical support for the scene classification of stacked garbage based on deep learning.

In the article are presented data devoted to the problems of municipal solid waste, as they are one of the most acute economic and environmental problems. In the cities, there is an intensive accumulation of solid waste and bulky waste, which, if improperly and untimely removal seriously pollute the environment. Creating a normal conditions of people lives in the city — the primary task of public utilities engaged in sanitary cleaning and cleaning of urban areas, the disposal of solid waste. Therefore, there is a need to develop new approaches in the system of waste management and the creation of modern components the relevant infrastructure in the field of solid waste management. According to the results of the literature review, the main objective is the modernization of regional waste management systems, using the best available technology to manage waste without causing unbearable cost for the user. The aim will be achieved by implementing an investment project for building in the area the waste management complex, consisting of the plant sorting and processing of waste and landfill, as well as the introduction of separate collection of waste from households and businesses.

This article deals with the problem of Municipal solid waste (MSW) in the Republic of Uzbekistan. In the process of research, the methods for the disposal of MSW that are still used in the country, such as incineration and disposal at landfills, were examined. For the necessity to implement innovations in the work of handling MSW, the basic morphological composition of MSW in the winter, spring, summer and autumn seasonal periods was researched. Based on the results obtained, an innovative direction is proposed for the processing of MSW into secondary raw materials in order to solve the problems of resource savings and jointly with the solution of environmental and economic problems of the republic. To solve the problem of waste management, an innovative technology has been proposed for the transition from landfills to waste recycling plants.

Qingdao is an important economic center in eastern coastal China. With the rapid urbanization process, problems of municipal solid waste (MSW) management have become a new concern for the government. In Qingdao, MSW collection and transportation (MSW-CT) amounts increased from 0.81 × 106 t/a in 1999 to 1.85 × 106 t/a in 2015. The per capita MSW production was nearly 1.1 kg/(capita·d), higher than the Chinese average value of 0.70 kg/(capita·d). The MSW management system of Qingdao was dominated by the “Municipal Public Bureau of Qingdao.” Due to the continuous reform of the sanitation management system, an operation mechanism of separation for government and enterprise, unified management, orderly competition and a three-level management system of “City – District – Street” were established. MSW-CT mode was mainly based on the combination of “multi-way collection in early, transfer station compression in late” and “compression car direct transport.” MSW treatment was mainly located at the “XiaoJianXi Solid Waste Comprehensive Disposal plant of Qingdao,” including sanitary landfill, incineration, composting, and recycling. Qingdao was one of the earliest pilot cities of MSW source separation in China. Although some relevant achievements were achieved, many problems were also found. In the future, MSW source separation will be regarded as a key concern for government departments in Qingdao. MSW management of Qingdao had always been leading position in the Shandong Province. However, some problems cannot be ignored, such as multi-head management, inadequate market competition, and inconsistent power between supervision and law enforcement. In the future, Qingdao’s MSW management will be improved through structural reform, financial investment, and an increase in market competition and public environmental awareness. A “Qingdao model” in Chinese MSW management will be gradually established.

Although there have been many studies related to the municipal solid waste (MSW) problem in Hong Kong, only limited research has recently discussed the spatial variation of citizens’ environmental awareness in different geographical constituencies. Also, the relationship between environmental awareness of citizens and the effectiveness of waste management has been poorly understood. This research aims to evaluate the effectiveness of three proposed approaches; investigate whether the environmental awareness of citizens will affect the effectiveness of current waste management strategies; and examine the differences in the level of environmental awareness of residents in different geographical constituencies, so we can provide suggestions to help mitigate the solid waste problem. In addition, the hypothesis is that there is a relationship between environmental awareness of citizens and its effectiveness. This research adopted quantitative observations and questionnaires as well as qualitative interviews to understand the public perception about the proposed MSW charging, plastic shopping bag charging and recycling in Hong Kong. This research confirmed that the environmental awareness of Hong Kong citizens varies among various geographical constituencies. Residents in Kowloon West have comparatively lower environmental awareness may due largely to the socio-economic background of residents and comparatively unhygienic environment, and they often do not support plastic shopping bag charging and recycling. In addition, males have a lower environmental awareness than females as their attitude towards charging policy may not have much impact on their lifestyle. It is not surprising that low environmental awareness of citizens will reduce the effectiveness of current environmental policies. MSW charging tends to be effective and should be implemented in Hong Kong. Furthermore, plastic shopping bag charging can be effective in the short term but ineffective in the long term, and recycling tends to be invalid as Hong Kong has not developed a circular economy. The limitations of each policy should be considered, and more studies are needed to improve the existing policies as well as exploring novel ways to solve the MSW problem.

Domestic waste classification is of great significance for building a beautiful living environment. Waste classification is a scientific management method for effective disposal of waste. Aiming at the problem of garbage pollution faced by human beings at present, a garbage classification algorithm based on deep learning is proposed.In this paper, we first build a garbage classification dataset, identify the categories of garbage items in the picture, and then build a garbage classification model. Through the comparison of different models, we test the accuracy of different models on the garbage classification results, and find a more effective and accurate garbage classification algorithm model.The experiment shows that this algorithm analyzes the existing model, preprocesses the data image, and then uses the image enhancement method to increase the robustness of the model used. It improves the model by adjusting the learning rate, label smoothing, and data normalization processing strategies. The optimized algorithm precision increases to 95%, and makes the model have better generalization ability.

Garbage detection and classification using a new deep learning-based machine vision system as a tool for sustainable waste recycling