Deep Learning Implemented Visualizing City Cleanliness Level by Garbage Detection

In an urban city,the daily challenges of managing cleanliness are the primary aspect of routine life,which requires a large number of resources,the manual process of labour,and budget.Street cleaning techniques include street sweepers going away to different metropolitan areas,manually verifying if the street required cleaning taking action.This research presents novel street garbage recognizing robotic navigation techniques by detecting the city’s street-level images and multi-level segmentation.For the large volume of the process,the deep learning-based methods can be better to achieve a high level of classifica-tion,object detection,and accuracy than other learning algorithms.The proposed Histogram of Oriented Gradients(HOG)is used to features extracted while using the deep learning technique to classify the ground-level segmentation process’s images.In this paper,we use mobile edge computing to process street images in advance andfilter out pictures that meet our needs,which significantly affect recognition efficiency.To measure the urban streets’cleanliness,our street clean-liness assessment approach provides a multi-level assessment model across differ-ent layers.Besides,with ground-level segmentation using a deep neural network,a novel navigation strategy is proposed for robotic classification.Single Shot Mul-tiBox Detector(SSD)approaches the output space of bounding boxes into a set of default boxes over different feature ratios and scales per attribute map location from the dataset.The SSD can classify and detect the garbage’s accurately and autonomously by using deep learning for garbage recognition.Experimental results show that accurate street garbage detection and navigation can reach approximately the same cleaning effectiveness as traditional methods.

Garbage sorting and disposal in Japan and its implications to China

Beginning from Shanghai,and then Beijing,China has started its garbage sorting enforcement.Before 2025,all cities above the prefecture level will set up and perfect their garbage sorting and disposal system.As Japan is the most successful example in garbage sorting and disposal in the world,where garbage sorting is different district from district,we put our focus on a district Nerima district,in Tokyo about its garbage classification and daily practice.It eventually aims to draw implications for China’s garbage sorting and disposal.As residents,we Chinese people can also learn from the Japanese daily practice in garbage sorting.Only after the government builds a reasonable gathering and disposing system,residents absolutely shoulder the responsibility of sorting garbage,can we succeed in garbage sorting enforcement and environment protection.

The Main Characteristics and Disposal of Municipal Garbage in Changchun City of China

Based on current situations of Chinese garbage management and disposal, this paper is centered on introduction of some research results of household garbage in Changchun, capital city of Jilin Province, northeast China. From the viewpoint of man the earth system, garbage is characterized by sociality, temporal and spatial differentiation and limitation and other features, which imply that the garbage research being involved with natural science and social science should not only focus on the natural features but also on the social features. Compared to some developed cities, the material composition of garbage in Changchun is typically characterized by high moisture content, low combustion value and minor recyclable materials. Concentration, cycle shaping and self similarity characterize the garbage distribution. The tracing investigation on 683 households ten times shows that three fourths of the total garbage yield in Changchun is derived from non domestic units and that the attention of garbage management, especially source reduction, should be focused on the non domestic units. The GM(1,1) prediction model has been set up and the prediction result shows that the amount of garbage yield of Changchun in 2004 will be up to 1,500,000 tons. Investigation of environmental consciousness to garbage problems have been carried out. Based on the above characteristics of garbage, the disposal countermeasures of Changchun garbage have been suggested.

YOLObin: Non-Decomposable Garbage Identification and Classification Based on YOLOv7

In order to safeguard the biological framework on which human creatures depend for presence and make society feasible improvement, it is becoming increasingly important to classify garbage. In any case, individuals are not recognizable with the classification strategy, so it is troublesome for individuals to accurately get the classification of each kind of rubbish. A proper waste management system is a primary task in building a smart and healthy city. In arrange to direct individuals to classify garbage accurately, this paper proposes a strategy of rubbish classification and acknowledgment based on YOLOv7 which is a cutting edge real time object detector. Performance of this model is compared along with two other object detectors where Mask-RCNN achieved f-measurement of 85%, YOLOv5 achieved f-measurement of 95.1% and YOLOv7 achieved f-measurement 95.9%. We have used non-decomposable multiclass garbage images which entails messy backgrounds with unwanted images as well. Four classes of non-decomposable garbage data namely chips packet, plastic bottle, polythene and image with multiclass garbage with 1000 images are prepared for our dataset. Our experimental models performed well in classifying garbage images with cluttered backgrounds. We compared our test results to previous studies in which the majority of the models were tested and trained using laboratory images. The test comes about illustrates that the classification framework features a sensible degree of accuracy and the segmentation recognition impact is way better within the case of point-by-point picture, which can proficiently and helpfully total the rubbish classification errand.

Research on the Applicability of Garbage Classification to Carbon Trading Market

Climate warming is one of the important environmental issues with global concern. The Bloomberg News has recorded temperature changes in the recent 135 years. As the hottest year, in 2014 the global surface temperature was as high as 1.39 degrees Fahrenheit, 0.68 degrees Celsius higher than the average in long-term. The severity of this issue has been proved with the refresh of the highest record^([1]) and the increasing temperature as well as people's personal experience. There is a demand of in-depth discussion about comprehensive and efficient reduction of carbon and greenhouse gas emission and the development of low carbon economy, with garbage classification as the most efficient breach but also most easily to be neglected by people. This article attempts to find a feasible method of carbon emission reduction from the perspective of garbage classification and resource recycling and make quantitative estimation of its value combined with local practice and data in Chengdu.

Antimicrobial Resistance Encountered in Garbage Collection Areas and Dumpsites in Nairobi, Kenya Using <i>Escherichia coli</i>and <i>Klebsiella</i>as Indicator Species

Dumpsites and garbage collection areas can act as reservoirs of highly resistant bacterial strains and facilitate the dissemination of Multidrug resistant strains to those living and work on or near the dumpsites and garbage collection areas. The objective of this study was to determine the potential of garbage collection areas and dumpsites in different parts of Nairobi as possible sources of resistant strains using E. coli and Klebsiella as indicator species. The study design was a cross-sectional survey. Sample collection was carried out at different days in seventeen different areas. A total of 126 samples were collected during the sampling period. The samples were then transported to the laboratory for analysis. The samples were cultured on MacConkey agar. Gram staining was done on discrete isolates based on colony characteristics. Biochemical tests were performed on colonies from primary cultures for final identification of the isolates. Antimicrobial disc susceptibility tests and pathogenicity tests were also carried out on the indicator isolates. A total of 121 E. coli and 165 Klebsiella were isolated from all the sampled sites. The highest bacterial burden was recorded from Muthurwa estate dumpsite, with a mean viable count of 8.2 × 1010 cfu/gm while the least was from Dandora dumpsite with a mean count of 1.1 × 1011 cfu/gm. Overall, gentamicin was the most effective antibacterial agent on Klebsiella and meropenem was the most effective on both E. coli and Klebsiella strains. The isolates showed high resistance to ampicillin, streptomycin, and trimethoprim-sulfamethoxazole. It is concluded that municipal waste dumpsites and garbage collection areas bear heavy burdens of potentially resistant bacteria which may constitute major public health hazards, not only to the immediate communities but also to the families of such site workers.

Practical Study of the Sports Garbage Pickup Tournament in Japan-Sports Events to Solve Social Problems

The Sports Garbage Pickup Tournament is a sports event that incorporates elements of competition into cleaning activities,as opposed to traditional cleaning activities.As of the end of December 2016,a total of 552 Sports Garbage Pickup Tournaments have been held,mobilizing a cumulative total number of 62,989 people.As each tournament brings a revenue of over 300,000 yen,the total sales so far would be more than 165,000,000 yen.This game was recently reported on the International Olympic Committee channel.The garbage issue,a social problem for local communities,should be solved by residents,visitors,and workers themselves through sports,cooperating with local governments.This is an eco-friendly sport aimed at“solving local social problems through sports.”Sports Garbage Pickup Tournaments can be held anywhere,inside the city or in a natural environment.In addition,the competition allows participants to visually understand the environmental capacity of garbage produced in the community,as the points are weighted according to the types of garbage.The Sports Garbage Pickup Tournament is an educational program for sustainable development with emphasis on experience,pursuit,and practice,and also an action-based program aimed at promoting spontaneous action.This is an optimal competition for participants to acquire skills through mutual communication.As children,adults,and people with disabilities can play together,the competition will promote communication across the generations.

Countermeasures on improving the charge rules and long-acting operation mechanism for the treating residential domestic sewage and household garbage in rural Yunnan

Improving the rural living environment in rural China is one of the key tasks for the country to accomplish its goal of building a moderately prosperous society by 2020. Yunnan has highly focused on the task of the treatment of residential domestic sewage and household garbage in rural area. Many efforts and resources have been put into this field in Yunnan since 2016. Progress has been made to increase the coverage rate of the sewage and household garbage treatment facilities. Seventy-five percent of total administrative villages have built up garbage transportation system and treatment facilities. Sixty-three percent of towns have collected and treated the residential domestic sewage by constructing various scale sewage treatment stations. However, the lack of the long-acting operation mechanism and the imperfection of the charge rules for the sewage and garbage treatment facilities have become problems that would hinder the achieving of the environmental goals in Yunnan. The reasons were elaborated on the basis of the local actual situations. Therefore, it is significant to improve the charge rules and frame the long-acting operation mechanism by strengthening the governance capacity, frame an overall mechanism and encourage the mass to be involved in the improvements of the living environment in rural Yunnan.

Application of Optimized BP Neural Network in Addressing for Garbage Power Plant

Neural network has the abilities of self-studying, self-adapting, fault tolerance and generalization. But there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain number of implied layer and implied notes. This paper presents a solution for overcoming these shortages from two aspects. One is to adopt principle component analysis to select study samples and make some of them contain sample characteristics as many as possible, the other is to train the network using Levenberg-Marquardt backward propagation algorithm. This new method was proved to be valid and practicable in site selection of practical garbage power generation plants.

Garbage乐队小传

说Garbage是一支乐队,倒不如说他们是一支制作人的团队更为合适.除了Shirly Manson这位魅力无比的女声主唱,三位男性成员Butch Vig、Duke Erikson和Billy Bush均是久已成名的音乐制作人.

Research on Business Model Optimization of Artificial Intelligence Garbage Classification

In recent years,garbage classification and environmental protection are gradually becoming an important step in the construction of ecological civilization in China.However,the popularity and commercial value of the application of artificial intelligence trash cans in Beijing are not high at present.This article analyzes these problems one by one and propose solutions,hoping that the commercial value of artificial intelligence trash cans can be optimized and improved and to make the city greener.This paper uses the questionnaire method and the literature method to research and analyze the optimization of the business model of artificial intelligence in garbage classification.

Beijing Declares War on Garbage

ＢｅｉｊｉｎｇＤｅｃｌａｒｅｓＷａｒｏｎＧａｒｂａｇｅ￥ＬＵＯＱＩＡＮ…ａｎｄａｆｏｒｍｉｄａｌｅｊｏｂｔｏｄｏ．ｂｙｓｔａｆｆｒｅｐｏｒｔｅｒＬＵＯＱＩＡＮＡＴＡＦＲＯＭａｎａｅｒｉａｌｒｅｍｏｔｅｓｅｎｓｉｎｇｔｅｓｔｃａｒｒｉｅｄｓｈｏｗｅｄｔ...

Keeping Foreign Garbage Out of China

ＫｅｅｐｉｎｇＦｏｒｅｉｇｎＧａｒｂａｇｅＯｕｔｏｆＣｈｉｎａＢｙｓｔａｆｆｒｅｐｏｒｔｅｒＸＩＡＥＲＴｈｅＳｔａｔｅＢｕｒｅａｕｏｆＥｎｖｉｒｏｎｍｅｎｔａｌＰｒｏｔｅｃｔｉｏｎａｎｄａｆｆｉｌｉａｔｅｄｄｅｐａｒｔｍｅｎｔｓｈｏｌｄａｎｏｎ－ｔｈ...

New Benchmark for Household Garbage Image Recognition

Household garbage images are usually faced with complex backgrounds,variable illuminations,diverse angles,and changeable shapes,which bring a great difficulty in garbage image classification.Due to the ability to discover problem-specific features,deep learning and especially convolutional neural networks(CNNs) have been successfully and widely used for image representation learning.However,available and stable household garbage datasets are insufficient,which seriously limits the development of research and application.Besides,the state-of-the-art in the field of garbage image classification is not entirely clear.To solve this problem,in this study,we built a new open benchmark dataset for household garbage image classification by simulating different lightings,backgrounds,angles,and shapes.This dataset is named 30 classes of household garbage images(HGI-30),which contains 18 000 images of 30 household garbage classes.The publicly available HGI-30 dataset allows researchers to develop accurate and robust methods for household garbage recognition.We also conducted experiments and performance analyses of the state-of-the-art deep CNN methods on HGI-30,which serves as baseline results on this benchmark.

A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot

To tackle the problem of aquatic environment pollution,a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory.We propose a garbage detection method based on a modified YOLOv4,allowing high-speed and high-precision object detection.Specifically,the YOLOv4 algorithm is chosen as a basic neural network framework to perform object detection.With the purpose of further improvement on the detection accuracy,YOLOv4 is transformed into a four-scale detection method.To improve the detection speed,model pruning is applied to the new model.By virtue of the improved detection methods,the robot can collect garbage autonomously.The detection speed is up to 66.67 frames/s with a mean average precision(mAP)of 95.099%,and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.

A similarity-guided segmentation model for garbage detection under road scene

The development of computer vision technology provides a possible path for realizing intelligent control of road sweepers to reduce energy waste in urban street cleaning work.For garbage segmentation of seven categories under road scene,we introduce an efficient deep-learning-based method.Our model follows a lightweight structure with a feature pyramid attention(FPA)module employed in the decoder to enhance feature integration at multi-levels.Besides,a similarity guidance(SG)module is added to the decoder branches,which calculates the cosine distance between learned prototypes and feature maps to guide the segmentation results from a metric learning perspective.Our model has less than 3 M parameters and can run at over 65 FPS in an RTX 2070 GPU.Experimental results demonstrate that our method can yield competitive results in terms of speed and accuracy trade-off,with overall mean intersection-over-union(mIoU)reaching 0.87 and 0.67,respectively,on two garbage data sets we built.Besides,our model can perform acceptable category-balanced segmentation from less than 20 annotated images per category by introducing the SG module.

Microbial inoculants and garbage fermentation liquid reduced root-knot nematode disease and As uptake in Panax quinquefolium cultivation by modulating rhizosphere microbiota community

Objective:To find a suitable ecological cultivation measure to solve the problem of root-knot nematode disease of Panax quinquefolium(Panacis Quinquefolii Radix)and the heavy metals accumulating in its roots.Methods:Three-year-old P.quinquefolium was treated with four different combinations of microbial inoculant(MI)and garbage fermentation liquid(GFL)[the joint application of‘TuXiu’MI and Fifty potassium MI(TF),the combination use of‘No.1'MI and Fifty potassium MI(NF),‘Gulefeng’poly-γ-glutamic acid MI(PGA),GFL],and the untreated control(CK).Here,high-throughput sequencing,ICP-MS and UPLC were employed to systematically characterize changes of microbial diversity and structure composition,heavy metals(As,Cd and Pb)content and ginsenoside content among different treatments.Results:The results revealed that different MIs and GFL could increase the root dry weight of P.quinquefolium,PGA enhanced it by 83.24%,followed by GFL(49.93%),meanwhile,PGA and GFL were able to lessen root-knot nematode disease incidence by 57.25%and 64.35%.The treatment of PGA and GFL can also effectively reduce heavy metals in roots.The As content in GFL and PGA was decreased by 52.17%and 43.48%respectively,while the Cd and Pb contents of GFL and PGA was decreased somewhat.Additionally,the content of total ginsenosides was increased by 42.14%and 42.07%,in response to TF and NF,respectively.Our metagenomic analysis showed that the relative abundance of particular soil microbial community members related to the biocontrol of root-knot nematode disease and plant pathogen(i.e.,Chaetomium in NF,Xylari in GFL,and Microascus in PGA),heavy metal bioremediation(Hyphomacrobium in PGA and Xylaria in GFL),and nitrogen fixation(Nordella and Nitrospira in TF)was significantly increased;notably,potential harmful microflora,such as Plectosaphaerella and Rhizobacter,were more abundant in the control group.Conclusion:MI and GFL could improve the quality of P.quinquefolium by modifying its rhizosphere microbial community structure and composition,both of them are beneficial to the development of ecological cultivation of P.quinquefolium.

Garbage Collection and Data Recovery for N2DB

Non-Volatile Memory(NVM) offers byte-addressability and persistency. Because NVM can be plugged into memory and provide low latency, it offers a new opportunity to build new database systems with a single-layer storage design. A single-layer NVM-Native DataBase(N2 DB) provides zero copy and log freedom. Hence, all data are stored in NVM and there is no extra data duplication and logging during execution. N2 DB avoids complex data synchronization and logging overhead in the two-layer storage design of disk-oriented databases and in-memory databases. Garbage Collection(GC) is critical in such an NVM-based database because memory leaks on NVM are durable. Moreover, data recovery is equally essential to guarantee atomicity, consistency, isolation, and durability properties. Without logging, it is a great challenge for N2 DB to restore data to a consistent state after crashes and recoveries. This paper presents the GC and data recovery mechanisms for N2 DB. Evaluations show that the overall performance of N2 DB is up to 3:6 higher than that of InnoDB. Enabling GC reduces performance by up to 10%,but saves storage space by up to 67%. Moreover, our data recovery requires only 0:2% of the time and half of the storage space of InnoDB.

MOHURD: Household Garbage Sorting Accelerated, Timetables for 46 Key Cities Clarified

MOHURD released the Notice on Accelerating the Household Garbage Sorting in Certain Key Cities,requiring pushing forward the household garbage sorting in 46 key cities such as Beijing,Tianjin,Shanghai,etc.,and establishing demonstration areas for garbage sorting in these cities for 2018.