Intelligent Garbage Research Articles

Intelligent waste sorting for sustainable environment: A hybrid deep learning and transfer learning model

The significance of waste disposal, classification, and monitoring has dramatically increased due to the increase in industrial development and the progress of intelligent urbanization. Since the last few decades, the utilization of Deep learning techniques has grown increasingly in waste management research. The efficiency of a waste reuse and recycling process relies on its capacity to restore resources to their original state, thereby minimizing pollution and promoting an ecologically sustainable framework. Selecting the optimal deep-learning method for classifying and predicting waste is challenging and time-consuming. This paper proposed intelligent garbage categorization using Bidirectional Long Short-Term Memory (Bi-LSTM) and CNN-based transfer learning to improve environmental sustainability. Organic and recyclable garbage are separated. To simplify trash categorization, a hybrid model combines TL-based CNN and Bi-LSTM. This study extensively examined the suggested technique with numerous CNN computational methods, including VGG-19, ResNet-34, AlexNet, ResNet-50, and VGG-16, using the ’TrashNet Waste’ database. Key findings show that our hybrid model outperforms existing models. Our classification accuracy is 96.78 %, 5.27 % higher than the best model. Our model also reduces misclassification by 7.25 %, proving its reliability. This comprehensive examination examined the computer models’ trash classification performance and provided specific viewpoints. The results explain each technique’s pros and cons and show how useful they are in real-world circumstances. Waste classification is practical and sophisticated with a hybrid model. The effectiveness and cleverness of this model improve sustainable environmental practices. The proposed method’s excellent performance suggests its seamless integration into practical waste management solutions.

Path Optimisation of Electric Vehicles for Collection and Delivery based on Intelligent Waste Bins

This paper proposes a dynamic electric vehicle path optimization method for intelligent garbage collection and transportation systems to address the increasing uncertainty of urban household waste and the carbon emissions caused by vehicles in traditional garbage collection and transportation processes. With the goal of minimizing total cost and maximizing garbage collection and transportation, adjust the soft time window in a timely manner based on whether the amount of garbage generated by the intelligent garbage bin reaches the collection and transportation threshold. Taking into account the position of the garbage bin to be cleared, the position of the garbage collection and transportation vehicle, the amount of electricity, and the loading capacity, dynamically adjust the vehicle path. Due to the fact that genetic algorithms require a long time to converge to an acceptable solution, this paper uses greedy genetic algorithms to comprehensively utilize their advantages in local search and global search capabilities to achieve faster convergence solutions. The research results indicate that compared with traditional static garbage collection and transportation modes, dynamic garbage collection and transportation schemes can reduce total costs and increase garbage collection and transportation volume, and reasonable planning of the path of electric collection and transportation vehicles can effectively reduce carbon emissions. Finally, the effectiveness and efficiency of the algorithm were verified through comparative analysis of numerical examples.

Intelligent robotic control system based on computer vision technology

Computer vision is a kind of simulation of biological vision using computers and related equipment. It is an important part of the field of artificial intelligence. Its research goal is to make computers have the ability to recognize three-dimensional environmental information through two-dimensional images. Computer vision is based on image processing technology, signal processing technology, probability statistical analysis, computational geometry, neural network, machine learning theory and computer information processing technology, through computer analysis and processing of visual information.The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency, quality, and environmental sustainability. The article also discusses methodologies for developing intelligent garbage sorting robots, emphasizing the application of computer vision image recognition, feature extraction, and reinforcement learning techniques. Overall, the integration of computer vision technology with robot control holds promise for enhancing human-computer interaction, intelligent manufacturing, and environmental protection efforts.

Comprehensive review of solid waste management strategies and disposal approaches

Solid waste management is a worldwide concern with substantial environmental, social, and economic ramifications. This review provides a complete assessment of solid waste management from an Indian viewpoint. It presents an overview of the current state, difficulties, and prospects in solid waste management, focusing on environmental sustainability and public health. The goal is to give a comprehensive overview of many approaches utilized to manage and reduce the expanding problem of solid waste. It examines a variety of waste management techniques, such as landfill disposal, recycling, composting, waste-to-energy practices, and waste elimination and minimizing. Many municipalities across the country prefer landfilling for the removal of solid waste. In landfills, waste is transformed through chemical, biological, and physical processes, functioning as an environmental furnace. Therefore, soil cover thickness, barriers, leachate collecting, landfill gas recovery, and flaring facilities are essential for sustainable landfilling. The benefits of composting and anaerobic breakdown for decreasing the environmental effect of biological waste are examined in this review. Hazardous chemicals in landfills can have severe ecological and public health consequences. It looks into the benefits of effective dangerous waste disposal, such as pollution control and resource recovery. Mining and reusing landfills for diverse uses, such as renewable energy generation and recreational areas, have the potential to be beneficial. The article discusses upcoming solid waste management trends and technologies such as intelligent garbage collection mechanisms, reuse and recycling concepts, and sustainable packaging solutions. It emphasizes the potential of these advances to improve overall waste management effectiveness and sustainability.

Research on smart city public health detection system and improvement technology based on intelligent multi-objective.

With the increase of urban population density, urban sanitation becomes more severe; urban sanitation has important influence on public health. Therefore, in order to realize the detection of public health in smart cities, the research will use cutting-edge scientific and technological methods to improve urban environmental health, so as to promote the realization of public health achievements. This study introduces public health detection and optimizationtechnologies for smart cities. Firstly, a data detection system for urban public health environment was established using sensors and intelligent multi-objective technology to evaluate the water quality, air quality, and noise level of the city. Then, an intelligent garbage management system based on Tensor-flow was constructed to achieve efficient garbage collection and treatment. Finally, an intelligent traffic management system was developed to monitor and regulate urban traffic flow. The results of the simulation experiment demonstrated that the life data detection system was operationally stable, with a high success rate of 98%. Furthermore, its accuracy in detecting residents' living environment data was above 95%, the maximum relative error was only 0.0465, making it a reliable and efficient tool. The waste recycling system achieved a minimum accuracy of 83.6%, the highest accuracy rate was 95.3%, making it capable of sorting and recycling urban waste effectively. Additionally, the smart traffic management system led to a 20% reduction in traffic congestion rates, 20 tonnes less tailpipe emissions and an improvement in public health and well-being. In summary, the plan proposed in this study aims to create a more comfortable, safe, and healthy urban public health environment, while providing theoretical support for environmental health management in smart cities.

A Garbage Intelligent Classification and Recycling System Based on Deep Learning

Garbage classification, if not properly implemented, may lead to environmental pollution during recycling. In order to overcome this problem effectively, an intelligent garbage classification and recycling system based on convolutional neural network (CNN) is introduced. In this paper, a well-designed deep network structure is used to optimize the model parameters, and the powerful computing power of graphics processing unit (GPU) is used to realize parallel processing and batch processing, which aims to improve the classification accuracy and processing speed of the system. According to the results of the study, the CNN-based garbage classification system demonstrated extremely high performance, with classification accuracy of up to 99% and a minimum of 94%. The system design significantly improves the accuracy of garbage classification. With the help of automatic feature learning and the capability of deep network architecture, the system can more accurately identify the key features in the garbage image, thus ensuring the reliability of the classification results and laying the foundation for the efficient recycling of garbage.

基于语音识别的破袋处理智能垃圾桶设计

基于语音识别的破袋处理智能垃圾桶设计

Raspberry Pi-based design of intelligent household classified garbage bin

Until now, waste pollution is a global problem. The first and foremost part of the solution is waste classification, as it can effectively reduce the output of waste, so it is particularly important to classify household garbage in the household. However, classification of household garbage is a difficult task for people, and there are not enough designs and researches on automatic garbage classification bins at home. In this study, we designed and built a light and convenient intelligent garbage bin using devices such as motors, turn plates and a camera for classifying household garbage. Compared with the traditional garbage classification methods, two improvements in the algorithm are made, including the combination of the EfficientNetB2 model with the parallel mixed-attention mechanism and the design of the background noise removal algorithm. Finally, the algorithm is disposed to the Raspberry Pi to compose the complete classification system. The accuracy of this classification system in the test set was 93.38% and the results show that this bin can effectively distinguish recyclable waste, kitchen waste, hazardous waste and other waste, and is suitable for daily life garbage identification at home.

SMART DUSTBIN MONITORING SYSTEM USING NodeMCU AND IOT

This paper presents an inexpensive, simple, and scalable intelligent garbage management system. The system uses ultrasonic sensors to measure fill levels and send notifications to garbage collectors when bins need to be emptied. The web-based interface allows waste collectors to monitor fill levels in real time. Smart dustbin monitoring systems have the potential to improve waste management by making it more efficient, sustainable and cost-effective. Smart dustbin monitoring systems use sensors and IoT technology to improve waste management by increasing collection frequency, saving money and improving efficiency. Keywords – Smart Dustbin, Smart City,NodeMCU, Smart System

Research on the historical evolution of urban street morphology under the background of big data and Internet of Things

With the advent of the era of Industry 4.0, big data and the Internet of Things came into being. The rise of big data and the Internet of Things has had an impact on all walks of life. The development of big data and the Internet of Things provides an opportunity for the evolution of street form. With the rapid development of smart cities, the street shape is also changing more and more towards the direction of intelligence. Taking Chunxi Road in Chengdu as an example, this paper studies the generation mechanism of street morphology. The factors influencing the generation of street form are divided into two categories: (1) legal factors. (2) Architectural design factors. By building a model, the article quantitatively analyzes the impact of these two types of factors on street form. At the same time, the article conducts an in-depth study on the evolution of ancient Chinese street morphology. The ancient Chinese street form is divided into four stages, namely the Tang and Song Dynasties, the Yuan, Ming and Qing Dynasties, the Late Qing Dynasty, and the founding of New China. The influencing factors of street shape are divided into four categories, namely, the influence of commodity economy, the influence of foreign culture, the influence of building materials, and the influence of traffic. This paper elaborates the influence mechanism of these four factors on the street form in China. The article finally puts forward the development trend of my country’s future street form, and points out that the evolution of my country’s street form will develop in the direction of intelligence, and intelligent transportation, intelligent garbage classification system, etc. will be applied. The number of brick-and-mortar stores in my country’s street layout may decrease.

Intelligent dustbin based on convolution neural network, spectroscopy and internet of things

Aiming at the problems of low level of intelligent automation of garbage classification, low flexibility, low efficiency, high cost and difficulty in large-scale production, this paper proposes a multimodal data fusion intelligent garbage bin system based on neural network, spectral technology and ultrasonic technology, which can effectively classify and recover different types of garbage. This paper mainly includes the following aspects: (1) The accuracy of MobileNetV2 was improved to 76.3% by using an experimental research method; (2) The near-infrared spectrum was studied by literature study and simulation method, and the characteristic wavelength reflectance ratio was used to establish the model; (3) The literature research method and quantitative analysis method were used to judge the types of solid and liquid waste according to the cavitation effect; (4) Integrate the above three technologies and design the corresponding intelligent trash can model, so that the system can perform multimodal data fusion according to the items put by users, jointly determine their categories and assign them to the corresponding categories of trash cans.

Machine Learning-Based Garbage Detection and 3D Spatial Localization for Intelligent Robotic Grasp

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.

Research On Intelligent Classification Trash Can Based on Machine Learning

At present, there are still many difficulties in advancing garbage classification in China. This passage simulates human eyes by camera, core processor simulates human brain, complete human motion by mechanical design, and realizes intelligent garbage bin classification based on machine vision: AI vision module takes K210 as the core processor; The electronic module uses STM32 as the core chip and the mechanical module to realize two main structures: stepper motor - turntable and servo - connecting rod, which can transport the identified garbage and put it into the corresponding trash can. After testing, the identification accuracy of harmful garbage is as high as 99%, and the accuracy of the other three types of garbage is 93.18%, which can effectively identify the types of garbage and realize automatic garbage classification.

Research on garbage sorting and smart bins

With the continuous innovation of science and technology, the quality of life of Chinese residents has been continuously improved. But behind the growing economy, there is a serious problem of garbage disposal. Aiming at the problems of garbage disposal and recycling, a Raspberry Pi is proposed as the base platform to build an intelligent garbage classification model to realize the design of intelligent garbage classification and recycling. With the help of AI to obtain garbage image recognition, improve the efficiency of garbage sorting, break through the limitation of space, minimize the investment of labor costs, realize smart construction, and have high promotion and application value.

Design of intelligent garbage classifier system based on machine vision

With the rapid development of society, the amount of garbage produced in life is increasing day by day. How to deal with the garbage and how to classify the garbage scientifically and effectively are hot topics in the current society. To solve the above problems, this paper proposes an intelligent garbage classifier based on machine vision. The design uses STM32F103C8T6 single chip microcomputer as the core controller of the intelligent garbage collector. The hardware part of the system is composed of computer vision identification module, power module, photoelectric switch module, ultrasonic module, stepper motor driver and other modules. At the same time, different kinds of garbage are identified through the visual module. The single-chip microcomputer completes the software test of garbage classification through the stepper motor. Finally, the performance of the intelligent garbage collector is verified through experiments. The results show that the intelligent garbage classifier can realize the key functions of intelligent identification and automatic sorting and achieve high accuracy in the process of identifying different garbage. The design has great practical value.

Design and Implementation of Intelligent Control Garbage Bin

Aiming at the problem that it was difficult to popularize accurate garbage sorting and delivery, a kind of intelligent garbage bin that is more intelligent, efficient, and functional than the traditional garbage bin was designed and manufactured. The design was based on the STM32 minimum system implementation. The speech recognition module was used to receive the key words and identify which type of garbage the item belongs to. At the same time, a speech reminder was given, and the steering gear was driven to automatically open the corresponding type of garbage bin lid, and the sorting collection was realized. In addition, the ultrasonic distance measuring module was used to detect the residual capacity of the garbage bin in real time. When it was about to be full, the data would be sent to the background management system through the WIFI module to remind the staff to clean it up. The experimental results show that the intelligent garbage bin can effectively help people sort and put domestic garbage and prevent garbage overflow from affecting the living environment, so as to realize the service of science and technology for human beings.

A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle

A population explosion has resulted in garbage generation on a large scale. The process of proper and automatic garbage collection is a challenging and tedious task for developing countries. This paper proposes a deep learning-based intelligent garbage detection system using an Unmanned Aerial Vehicle (UAV). The main aim of this paper is to provide a low-cost, accurate and easy-to-use solution for handling the garbage effectively. It also helps municipal corporations to detect the garbage areas in remote locations automatically. This automation was derived using two Convolutional Neural Network (CNN) models and images of solid waste were captured by the drone. Both models were trained on the collected image dataset at different learning rates, optimizers and epochs. This research uses symmetry during the sampling of garbage images. Homogeneity regarding resizing of images is generated due to the application of symmetry to extract their characteristics. The performance of two CNN models was evaluated with the state-of-the-art models using different performance evaluation metrics such as precision, recall, F1-score, and accuracy. The CNN1 model achieved better performance for automatic solid waste detection with 94% accuracy.

Intelligent garbage classification system based on improve MobileNetV3-Large

In response to the call for implementing national waste classification, this paper proposes an intelligent waste classification system based on the improved MobileNetV3-Large, which can raise the national awareness of waste classification through the combination of software and hardware. The software module is based on WeChat applet and offers functions for image recognition, text recognition, speech recognition, points-based quiz and so on. The hardware module is based on Raspberry Pi and covers image shooting, image recognition, automatic classification with automatic announcement and so on. The algorithm model applied to the image classification adopts a network model based on MobileNetV3-Large. This network model is enabled to classify garbage images through deep separable convolution, inverse residual structure, lightweight attention structure and the hard_ swish activation function. The text classification model adopts a network model based on LSTM, extracts text features through word embedding, enhancing the effect of garbage text classification. After testing, the system can leverage deep learning to realise intelligent garbage classification. The image recognition accuracy of the algorithm model was found to reach 81%, while the text recognition accuracy was as high as 97.61%.

Design of Intelligent Garbage Classification System Based on Internet of Things Technology

With the increasingly prominent problem of environmental pollution, it is extremely urgent to carry out garbage classification. This paper designs an intelligent garbage classification system based on Internet of Things technology, The system is mainly composed of relay driving circuit, infrared induction, metal detection and humidity detection modules. Single chip microcomputer and multi-channel sensors are used to collect and process related data to realize metal garbage recovery, dry garbage and wet garbage classification and delivery, and the collected related data are displayed on the display screen through serial communication. The experimental results show that the system has the characteristics of simple structure, stable performance and convenient operation, which provides a feasible solution for the current garbage classification and treatment.

Design of an Intelligent Garbage Container that Allows to Monitor the Waste for its Proper Collection

The increase in garbage production today is due to the exponential growth of the population worldwide, due to the fact that thousands of tons of garbage are generated daily around the world, but the mismanagement that gives them has become an environmental problem since 33% of all the garbage generated is not recycled, for that reason it is estimated that within the next three decades the amount of waste worldwide will increase to 70%. That is why in the present research work it is proposed to make an intelligent system based on the Internet of Things (IoT) that allows monitoring the garbage containers in real time representing with percentages the state of these containers and these can be collected in time by garbage trucks, and thus avoid the increase of garbage in the streets and the various types of problems that these would cause. As a result, it was obtained that the System does comply with the established conditions because it allows to monitor in real time representing by percentages the state of the garbage container, which indicates 40% as almost full and 80% indicates that it is already available for collection. Finally, it is concluded that using the Garbage Container Monitoring System will allow to better optimize the collection process and, in addition, the problems that are usually perceived today due to the amount of garbage that are registered in the streets will decrease. Keywords-- Internet of Things; Intelligent system; Real time; Environmental Problem; Monitoror; Percentage.