Search Results (42)

The enormous increase in the volume of waste caused by the population boom in cities is placing a considerable burden on waste processing in cities. The inefficiency and high costs of conventional approaches exacerbate the risks to the environment and human health. This article proposes a thorough approach that combines deep learning models, IoT technologies, and easily accessible resources to overcome these challenges. Our main goal is to advance a framework for intelligent waste processing that utilizes Internet of Things sensors and deep learning algorithms. The proposed framework is based on Raspberry Pi 4 with a camera module and TensorFlow Lite version 2.13. and enables the classification and categorization of trash in real time. When trash objects are identified, a servo motor mounted on a plastic plate ensures that the trash is sorted into appropriate compartments based on the model’s classification. This strategy aims to reduce overall health risks in urban areas by improving waste sorting techniques, monitoring the condition of garbage cans, and promoting sanitation through efficient waste separation. By streamlining waste handling processes and enabling the creation of recyclable materials, this framework contributes to a more sustainable waste management system. Full article

Sustainable and green waste management has become increasingly crucial due to the rising volume of waste driven by urbanization and population growth. Deep learning models based on image recognition offer potential for advanced waste classification and recycling methods. However, traditional image recognition approaches usually rely on single-label images, neglecting the complexity of real-world waste occurrences. Moreover, there is a scarcity of recognition efforts directed at actual municipal waste data, with most studies confined to laboratory settings. Therefore, we introduce an efficient Query2Label (Q2L) framework, powered by the Vision Transformer (ViT-B/16) as its backbone and complemented by an innovative asymmetric loss function, designed to effectively handle the complexity of multi-label waste image classification. Our experiments on the newly developed municipal waste dataset “Garbage In, Garbage Out”, which includes 25,000 street-level images, each potentially containing up to four types of waste, showcase the Q2L framework’s exceptional ability to identify waste types with an accuracy exceeding 92.36%. Comprehensive ablation experiments, comparing different backbones, loss functions, and models substantiate the efficacy of our approach. Our model achieves superior performance compared to traditional models, with a mean average precision increase of up to 2.39% when utilizing the asymmetric loss function, and switching to ViT-B/16 backbone improves accuracy by 4.75% over ResNet-101. Full article

In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the “garbage in, garbage out” issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians’ hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks. Full article

The explosive growth of massive data makes shingled magnetic recording (SMR) disks a promising candidate for balancing capacity and cost. SMR disks are typically configured with a persistent buffer to reduce the read–modify–write (RMW) overhead introduced by non-sequential writes. Traditional SMR zones-based persistent buffers are subject to sequential-write constraints, and frequent cleanups cause disk performance degradation. Conventional magnetic recording (CMR) zones with in-place update capabilities enable less frequent cleanups and are gradually being used to construct persistent buffers in certain SMR disks. However, existing CMR zones-based persistent buffer designs fail to accurately capture hot blocks with long update periods and are limited by an inflexible data layout, resulting in inefficient cleanups. To address the above issues, we propose a strategy called Amphisbaena. First, a two-phase data block classification method is proposed to capture frequently updated blocks. Then, a locality-aware buffer space management scheme is developed to dynamically manage blocks with different update frequencies. Finally, a latency-sensitive garbage collection policy based on the above is designed to mitigate the impact of cleanup on user requests. Experimental results show that Amphisbaena reduces latency by an average of 29.9% and the number of RMWs by an average of 37% compared to current state-of-the-art strategies. Full article

This paper proposes a framework for AI-driven municipal solid waste classification service design and management, with an emphasis on advancing sustainable urban development. This study uses narrative research and case study methods to delve into the benefits of AI technology in waste classification systems. The framework includes intelligent recognition, management strategies, AI-based waste classification technologies, service reforms, and AI-powered customer involvement and education. Our research indicates that AI technology can improve accuracy, efficiency, and cost-effectiveness in waste classification, contributing to environmental sustainability and public health. However, the effectiveness of AI applications in diverse city contexts requires further verification. The framework holds theoretical and practical significance, offering insights for future service designs of waste management and promoting broader goals of sustainable urban development. Full article

In recent decades, with the rising living standards of rural China, the amount and volume of household waste has increased continuously, causing serious environmental and human health risks. Effective garbage classification reduces garbage volume, decreases the difficulty of garbage disposal, and facilitates the recycling of resources, thereby improving environmental quality. Domestic garbage classification (DGC) has been practiced frequently in developed countries and is now at a relatively mature stage. There is no robust model for garbage classification available globally as of yet, and each country has its policy frameworks to reduce, recycle, and reuse (3R) garbage. Little attention has been paid to knowing whether and to what extent incentive-based policies called “rewards and punishments” improve garbage classification and further help achieve targets of sustainable development goals (SDGs). Recently, developing countries, like China, have begun to incorporate DGC into their laws and promote enforcement measures in a few cities. However, empirical studies on residents’ willingness to accept DGC punishments and rewards are still relatively scarce and a hot topic of global scientific discussion. To enrich the knowledge, this study collected datasets from 9983 valid questionnaires from east China (16 selected independent variables), and analyzed the key factors affecting residents’ acceptance of punishments and rewards, employing logit models. The results found that the level of education plays an important role for residents that are more inclined to accept DGC rewards and punishments. Moreover, farmers were insensitive to DGC rewards but very sensitive and unsupportive of punishments, and the hardware facilities of the quarter had a greater impact on residents’ willingness to accept DGC rewards and punishments. Findings recommend that rewards be the main focus and punishments be supplemented, thus the incentive-based policies should be improved through law enforcement and implementation of robust policy frameworks in order to promote residents’ acceptance of rewards and punishments and to accelerate better garbage classification. Full article

The scientific investment decision model of waste incineration power generation is helpful in providing a scientific basis for the government and environmental protection enterprises to formulate reasonable waste prices. The waste incineration power generation project revenue and cost composition framework, based on the project net present value of factors affecting causality analysis, the construction of a waste incineration power generation PPP project net present value system dynamics model, and the use of Vensim PLE software, version 7.3.5, combined with the garbage power generation of listed companies, was built, and we put into use the enterprise’s financial data and the author’s research of the case of the BOT (Build–Operate–Transfer) project data to examine the validity of the model test, simulation, and sensitivity analysis. The results show that the regression of a national subsidy does not necessarily lead to a price adjustment of the waste disposal fee, and when a change in tariff subsidy occurs, the loss brought by the reduction in a feed-in tariff can be compensated by increasing the income from carbon sinks, decreasing the intensity of investment through technological advancement, improving the coefficient of waste power generation through garbage classification, and increasing the utilization of production capacity through the treatment of multiple wastes in a single or a combination of ways. Full article

For effective pothole control on national highways, autonomous pothole identification technology utilizing artificial intelligence was deployed in Korea. There are a number of different objects on the road’s surface that resemble potholes. The YOLOv7-E6E model was used to reduce noise, before classifying these objects and potholes. In the algorithm, aberrant objects other than potholes were classified using design and learning techniques. Manhole, automobile, lane-marking, garbage, and shadow elements that are similar to potholes were learned, in order to detect them. “Etc.” was used to summarize 15 characteristics, including a broken patch, spalling, crack, ramp, license plate, leaf, and pool. In light of this, learning was conducted using a total of seven classification criteria. The test dataset had a 91% accuracy rate. Full article

A startling shift in waste composition has been brought on by a dramatic change in lifestyle, the quick expansion of consumerism brought on by fierce competition among producers of consumer goods, and revolutionary advances in the packaging sector. The overflow or overspill of garbage from the bins causes poison to the soil, and the total obliteration of waste generated in the area or city is unknown. It is challenging to pinpoint with accuracy the specific sort of garbage waste; predictive image classification is lagging, and the existing approach takes longer to identify the specific garbage. To overcome this problem, image classification is carried out using a modified ResNeXt model. By adding a new block known as the “horizontal and vertical block,” the proposed ResNeXt architecture expands on the ResNet architecture. Each parallel branch of the block has its own unique collection of convolutional layers. Before moving on to the next layer, these branches are concatenated together. The block’s main goal is to expand the network’s capacity without considerably raising the number of parameters. ResNeXt is able to capture a wider variety of features in the input image by using parallel branches with various filter sizes, which improves performance on image classification. Some extra dense and dropout layers have been added to the standard ResNeXt model to improve performance. In order to increase the effectiveness of the network connections and decrease the total size of the model, the model is pruned to make it smaller. The overall architecture is trained and tested using garbage images. The convolution neural Network is connected with a modified ResNeXt that is trained using images of metal, trash, and biodegradable, and ResNet 50 is trained using images of non-biodegradable, glass, and hazardous images in a parallel way. An input image is fed to the architecture, and the image classification is achieved simultaneously to identify the exact garbage within a short time with an accuracy of 98%. The achieved results of the suggested method are demonstrated to be superior to those of the deep learning models already in use when compared to a variety of existing deep learning models. The proposed model is implemented into the hardware by designing a three-component smart bin system. It has three separate bins; it collects biodegradable, non-biodegradable, and hazardous waste separately. The smart bin has an ultrasonic sensor to detect the level of the bin, a poisonous gas sensor, a stepper motor to open the lid of the bin, a solar panel for battery storage, a Raspberry Pi camera, and a Raspberry Pi board. The levels of the bin are maintained in a centralized system for future analysis processes. The architecture used in the proposed smart bin properly disposes of the mixed garbage waste in an eco-friendly manner and recovers as much wealth as possible. It also reduces manpower, saves time, ensures proper collection of garbage from the bins, and helps attain a clean environment. The model boosts performance to predict waste generation and classify it with an increased 98.9% accuracy, which is more than the existing system. Full article

Garbage detection and 3D spatial localization play a crucial role in industrial applications, particularly in the context of garbage trucks. However, existing approaches often suffer from limited precision and efficiency. To overcome these challenges, this paper presents an algorithmic architecture that leverages advanced techniques in computer vision and machine learning. The proposed approach integrates cutting-edge computer vision methodologies to improve the precision of waste classification and spatial localization. By utilizing RGB-D data captured by the RealSenseD415 camera, the algorithm incorporates state-of-the-art computer vision algorithms and machine learning models, including the Yolactedge model, for real-time instance segmentation of garbage objects based on RGB images. This enables the accurate prediction of garbage class and the generation of masks for each instance. Furthermore, the predicted masks are utilized to extract the point cloud corresponding to the garbage instances. The oriented bounding boxes of the segmented point cloud is calculated as the spatial location information of the garbage instances using the DBSCAN clustering algorithm to remove the interfering points. The findings indicate that the proposed approach can run at a maximum speed of 150 FPS. The usefulness of the proposed method in achieving accurate garbage recognition and spatial localization in a vision-driving robot grasp system has been tested experimentally on datasets that were custom-collected. The results demonstrate the algorithmic architecture’s ability to transform waste management procedures while also enabling intelligent garbage sorting and enabling robotic grasp applications. Full article

As the global population grows and urbanization accelerates, the garbage that is generated continues to increase. This waste causes serious pollution to the ecological environment, affecting the stability of the global environmental balance. Garbage detection technology can quickly and accurately identify, classify, and locate many kinds of garbage to realize the automatic disposal and efficient recycling of waste, and it can also promote the development of a circular economy. However, the existing garbage detection technology has some problems, such as low precision and a poor detection effect in complex environments. Although YOLOv5 has achieved good results in garbage detection, the detection results cannot meet the requirements in complex scenarios, so this paper proposes a garbage detection model, YOLOv5-OCDS, based on an improved YOLOv5. Replacing the partial convolution in the neck with Omni-Dimensional Dynamic Convolution (ODConv) improves the expressiveness of the model. The C3DCN structure is constructed, and parts of the C3 structures in the neck are replaced by C3DCN structures, allowing the model to better adapt to object deformation and target scale change. The decoupled head is used for classification and regression tasks so that the model can learn each class’s characteristics and positioning information more intently, and flexibility and extensibility can be improved. The Soft Non-Maximum Suppression (Soft NMS) algorithm can better retain the target’s information and effectively avoid the problem of repeated detection. The self-built garbage classification dataset is used for related experiments, and the mAP@50 of the YOLOv5-OCDS model is 5.3% higher than that of the YOLOv5s; the value of mAP@50:95 increases by 12.3%. In the experimental environment of this study, the model’s Frames Per Second (FPS) was 61.7 f/s. In practical applications, when we use some old GPU, such as the GTX1060, it can still reach 50.3 f/s, so that real-time detection can be achieved. Thus, the improved model suits garbage detection tasks in complex environments. Full article

Effective waste management and recycling are essential for sustainable development and environmental conservation. It is a global issue around the globe and emerging in Saudi Arabia. The traditional approach to waste sorting relies on manual labor, which is both time-consuming, inefficient, and prone to errors. Nonetheless, the rapid advancement of computer vision techniques has paved the way for automating garbage classification, resulting in enhanced efficiency, feasibility, and management. In this regard, in this study, a comprehensive investigation of garbage classification using a state-of-the-art computer vision algorithm, such as Convolutional Neural Network (CNN), as well as pre-trained models such as DenseNet169, MobileNetV2, and ResNet50V2 has been presented. As an outcome of the study, the CNN model achieved an accuracy of 88.52%, while the pre-trained models DenseNet169, MobileNetV2, and ResNet50V2, achieved 94.40%, 97.60%, and 98.95% accuracies, respectively. That is considerable in contrast to the state-of-the-art studies in the literature. The proposed study is a potential contribution to automating garbage classification and to facilitating an effective waste management system as well as to a more sustainable and greener future. Consequently, it may alleviate the burden on manual labor, reduce human error, and encourage more effective recycling practices, ultimately promoting a greener and more sustainable future. Full article

The classification and recycling of household waste becomes a major issue in today’s urban environmental protection and domestic waste disposal. Although various policies promoting household waste classification have been introduced, the recovery rate failed to reach the expected result. Existing studies on incentive policies for household waste recycling tried to integrate subjective and objective factors in human behavior decisions. To explore how effective interventions can promote household waste classification in communities, this article developed an Agent-Based Model (ABM) based on Theory of Planned Behavior (TPB) to simulate the participation of households under eight different policy scenarios. The result shows that: monetary incentive is most effective in inducing participation, while social norms have different impacts on household decision under different policy intervention. Under policy stimulus, the participation rate of garbage sorting increased from 18% to 76%. This model has been applied into an online community-based participatory virtual simulation 3D system, which aims to help university students better understand how policies affect household recycling behaviors, which end up affecting the environment. Full article

In China, waste sorting has gradually entered “compulsory times”. The beginning of the compulsory times of waste sorting is marked by the implementation of the policy to remove trash cans in residential building hallways. Since then, this policy has been controversial. Based on the theory of planned behavior and the public’s perspective as well as using the Delphi method and entropy weight method, we investigated Longhua District in Shenzhen and designed an evaluation index system for the effect of the policy to remove trash cans from the following three dimensions: the policy cognitive level, the policy admissive degree, and the awareness of waste sorting. The data were supplemented by observations and interview methods as well as questionnaire surveys that were distributed in residential sub-districts in Longhua District. According to the quantitative research and variance analysis of the data, the policy promotes waste sorting. This paper provides a new idea on how to evaluate environmental policies such as the garbage-classification policy from the perspective of the public based on rigorous evaluation methods and processes. Full article

Massive human population, coupled with rapid urbanization, results in a substantial amount of garbage that requires daily collection. In urban areas, garbage often accumulates around dustbins without proper disposal at regular intervals, creating an unsanitary environment for humans, plants, and animals. This situation significantly degrades the environment. To address this problem, a Smart Waste Management System is introduced in this paper, employing machine learning techniques for air quality level classification. Furthermore, this system safeguards garbage collectors from severe health issues caused by inhaling harmful gases emitted from the waste. The proposed system not only proves cost-effective but also enhances waste management productivity by categorizing waste into three types: wet, dry, and metallic. Ultimately, by leveraging machine learning techniques, we can classify air quality levels and garbage weight into distinct categories. This system is beneficial for improving the well-being of individuals residing in close proximity to dustbins, as it enables constant monitoring and reporting of air quality to relevant city authorities. Full article