Content-based image retrieval system for solid waste bin level detection and performance evaluation

Data Science Applications in Circular Economy: Trends, Status, and Future

The circular economy (CE) aims to decouple the growth of the economy from the consumption of finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due to the rapid development of data science (DS), promising progress has been made in the transition toward CE in the past decade. DS offers various methods to achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize the infrastructure needed to circulate materials, and provide evidence-based insights. Despite the exciting scientific advances in this field, there still lacks a comprehensive review on this topic to summarize past achievements, synthesize knowledge gained, and navigate future research directions. In this paper, we try to summarize how DS accelerated the transition to CE. We conducted a critical review of where and how DS has helped the CE transition with a focus on four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency and optimizing product design, (3) extending product lifetime through repair, and (4) facilitating waste reuse and recycling. We also introduced the limitations and challenges in the current applications and discussed opportunities to provide a clear roadmap for future research in this field.

GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management

The escalating waste volume due to urbanization and population growth has underscored the need for advanced waste sorting and recycling methods to ensure sustainable waste management. Deep learning models, adept at image recognition tasks, offer potential solutions for waste sorting applications. These models, trained on extensive waste image datasets, possess the ability to discern unique features of diverse waste types. Automating waste sorting hinges on robust deep learning models capable of accurately categorizing a wide range of waste types. In this study, a multi-stage machine learning approach is proposed to classify different waste categories using the "Garbage In, Garbage Out" (GIGO) dataset of 25,000 images. The novel Garbage Classifier Deep Neural Network (GCDN-Net) is introduced as a comprehensive solution, adept in both single-label and multi-label classification tasks. Single-label classification distinguishes between garbage and non-garbage images, while multi-label classification identifies distinct garbage categories within single or multiple images. The performance of GCDN-Net is rigorously evaluated and compared against state-of-the-art waste classification methods. Results demonstrate GCDN-Net's excellence, achieving 95.77% accuracy, 95.78% precision, 95.77% recall, 95.77% F1-score, and 95.54% specificity when classifying waste images, outperforming existing models in single-label classification. In multi-label classification, GCDN-Net attains an overall Mean Average Precision (mAP) of 0.69 and an F1-score of 75.01%. The reliability of network performance is affirmed through saliency map-based visualization generated by Score-CAM (class activation mapping). In conclusion, deep learning-based models exhibit efficacy in categorizing diverse waste types, paving the way for automated waste sorting and recycling systems that can mitigate costs and processing times.

Artificial intelligence applications for sustainable solid waste management practices in Australia: A systematic review

Solid waste generation and its impact on human health and the environment have long been a matter of concern for governments across the world. In recent years, there has been increasing emphasis on resource recovery (reusing, recycling and extracting energy from waste) using more advanced approaches such as artificial intelligence (AI) in Australia. AI is a powerful technology that is increasingly gaining popularity and application in various fields. The adoption of AI techniques offers alternative innovative approaches to solid waste management (SWM). Although there are previous studies on AI technologies and SWM, no study has assessed the adoption of AI applications in solving the diverse SWM problems for achieving sustainable waste management in Australia. Moreover, there are inconsistencies and a lack of awareness on how AI technologies function in relation to their application to SWM. This study examines the application of AI technologies in various areas of SWM (generation, sorting, collection, vehicle routing, treatment, disposal and waste management planning) to enhance sustainable waste management practices in Australia. To achieve the aims of this study, prior studies from 2005 to 2021 from various databases are collected and analyzed. The study focuses on the adoption of AI applications on SWM, compares the performance of AI applications, explores the benefits and challenges, and provides best practice recommendations on how resource efficiency can be optimized to improve economic, environmental and social outcomes. This study found that AI-based models have better prediction abilities when compared to other models used in forecasting solid waste generation and recycling. Findings show that waste generation in Australia has been steadily increasing and requires upgraded and improved recovery infrastructure and the appropriate adoption of AI technologies to enhance sustainable SWM. Australia's adoption of AI recycling technologies would benefit from a national approach that seeks consistency across jurisdictions, while catering for regional differences. This study will benefit researchers, governments, policy-makers, municipalities and other waste management organizations to increase current recycling rates, eliminate the need for manual labor, reduce costs, maximize efficiency, and transform the way we approach the management of solid waste.

Computer vision for solid waste sorting: A critical review of academic research

Waste sorting is highly recommended for municipal solid waste (MSW) management. Increasingly, computer vision (CV), robotics, and other smart technologies are used for MSW sorting. Particularly, the field of CV-enabled waste sorting is experiencing an unprecedented explosion of academic research. However, little attention has been paid to understanding its evolvement path, status quo, and prospects and challenges ahead. To address the knowledge gap, this paper provides a critical review of academic research that focuses on CV-enabled MSW sorting. Prevalent CV algorithms, in particular their technical rationales and prediction performance, are introduced and compared. The distribution of academic research outputs is also examined from the aspects of waste sources, task objectives, application domains, and dataset accessibility. The review discovers a trend of shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were unevenly distributed in different sectors such as household, commerce and institution, and construction. Too often, researchers reported some preliminary studies using simplified environments and artificially collected data. Future research efforts are encouraged to consider the complexities of real-world scenarios and implement CV in industrial waste sorting practice. This paper also calls for open sharing of waste image datasets for interested researchers to train and evaluate their CV algorithms.

A Study on AI‐based Waste Management Strategies for the COVID‐19 Pandemic

COVID‐19 has swept across the globe and disrupted all vectors of social life. Every informed measure must be taken to stop its spread, bring down number of new infections and move to normalization of daily life. Contemporary research has not identified waste management as one of the critical transmission vectors for COVID‐19 virus. However, most underdeveloped countries are facing problems in waste management processes due to the general inadequacy and inability of waste management. In that context, smart intervention will be needed to contain possibility of the COVID‐19 spread due to inadequate waste management. This paper presents a comparative study of the artificial intelligence/machine learning based techniques, and potential applications in the COVID‐19 waste management cycle (WMC). A general integrated solid waste management (ISWM) strategy is mapped for both short‐term and long‐term goals of COVID‐19 WMC, making use of the techniques investigated. By aligning current health/waste‐related guidelines from health organizations and governments worldwide and contemporary, relevant research in area, the challenge of COVID‐19 waste management and, subsequently, slowing the pandemic down may be assisted.

Computer vision to recognize construction waste compositions: A novel boundary-aware transformer (BAT) model

Recognition of construction waste compositions using computer vision (CV) is increasingly explored to enable its subsequent management, e.g., determining chargeable levy at disposal facilities or waste sorting using robot arms. However, the applicability of existing CV-enabled construction waste recognition in real-life scenarios is limited by their relatively low accuracy, characterized by a failure to distinguish boundaries among different waste materials. This paper aims to propose a novel boundary-aware Transformer (BAT) model for fine-grained composition recognition of construction waste mixtures. First, a pre-processing workflow is devised to separate the hard-to-recognize edges from the background. Second, a Transformer structure with a self-designed cascade decoder is developed to segment different waste materials from construction waste mixtures. Finally, a learning-enabled edge refinement scheme is used to fine-tune the ignored boundaries, further boosting the segmentation precision. The performance of the BAT model was evaluated on a benchmark dataset comprising nine types of materials in a cluttered and mixture state. It recorded a 5.48% improvement of MIoU (mean intersection over union) and 3.65% of MAcc (Mean Accuracy) against the baseline. The research contributes to the body of interdisciplinary knowledge by presenting a novel deep learning model for construction waste material semantic segmentation. It can also expedite the applications of CV in construction waste management to achieve a circular economy.

A deep learning approach based hardware solution to categorise garbage in environment

Garbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.

Application of artificial neural networks for predicting the physical composition of municipal solid waste: An assessment of the impact of seasonal variation

Sustainable planning of waste management is contingent on reliable data on waste characteristics and their variation across the seasons owing to the consequential environmental impact of such variation. Traditional waste characterization techniques in most developing countries are time-consuming and expensive; hence the need to address the issue from a modelling approach arises. In modelling the complexity within the system, a paradigm shift from the classical models to the intelligent models has been observed. The application of artificial intelligence models in waste management is gaining traction; however its application in predicting the physical composition of waste is still lacking. This study aims at investigating the optimal combinations of network architecture, training algorithm and activation functions that accurately predict the fraction of physical waste streams from meteorological parameters using artificial neural networks. The city of Johannesburg was used as a case study. Maximum temperature, minimum temperature, wind speed and humidity were used as input variables to predict the percentage composition of organic, paper, plastics and textile waste streams. Several sub-models were stimulated with combination of nine training algorithms and four activation functions in each single hidden layer topology with a range of 1-15 neurons. Performance metrics used to evaluate the accuracy of the system are, root mean square error, mean absolute deviation, mean absolute percentage error and correlation coefficient (R). Optimal architectures in the order of input layer-number of neurons in the hidden layer-output layer for predicting organic, paper, plastics and textile waste were 4-10-1, 4-14-1, 4-5-1 and 4-8-1 with R-values of 0.916, 0.862, 0.834 and 0.826, respectively at the testing phase. The result of the study verifies that waste composition prediction can be done in a single hidden-layer satisfactorily.

FIKWaste: A Waste Generation Dataset from Three Restaurant Kitchens in Portugal

In the era of big data and artificial intelligence, public datasets are becoming increasingly important for researchers to build and evaluate their models. This paper presents the FIKWaste dataset, which contains time series data for the volume of waste produced in three restaurant kitchens in Portugal. Organic (undifferentiated) and inorganic (glass, paper, and plastic) waste bins were monitored for a consecutive period of four weeks. In addition to the time series measurements, the FIKWaste dataset contains labels for waste disposal events, i.e., when the waste bins are emptied, and technical and non-technical details of the monitored kitchens.

Artificial intelligence applications in solid waste management: A systematic research review

The waste management processes typically involve numerous technical, climatic, environmental, demographic, socio-economic, and legislative parameters. Such complex nonlinear processes are challenging to model, predict and optimize using conventional methods. Recently, artificial intelligence (AI) techniques have gained momentum in offering alternative computational approaches to solve solid waste management (SWM) problems. AI has been efficient at tackling ill-defined problems, learning from experience, and handling uncertainty and incomplete data. Although significant research was carried out in this domain, very few review studies have assessed the potential of AI in solving the diverse SWM problems. This systematic literature review compiled 85 research studies, published between 2004 and 2019, analyzing the application of AI in various SWM fields, including forecasting of waste characteristics, waste bin level detection, process parameters prediction, vehicle routing, and SWM planning. This review provides comprehensive analysis of the different AI models and techniques applied in SWM, application domains and reported performance parameters, as well as the software platforms used to implement such models. The challenges and insights of applying AI techniques in SWM are also discussed.

Application of deep learning object classifier to improve e-waste collection planning

This study investigates an image recognition system for the identification and classification of waste electrical and electronic equipment from photos. Its main purpose is to facilitate information exchange regarding the waste to be collected from individuals or from waste collection points, thereby exploiting the wide acceptance and use of smartphones. To improve waste collection planning, individuals would photograph the waste item and upload the image to the waste collection company server, where it would be recognized and classified automatically. The proposed system can be operated on a server or through a mobile app. A novel method of classification and identification using neural networks is proposed for image analysis: a deep learning convolutional neural network (CNN) was applied to classify the type of e-waste, and a faster region-based convolutional neural network (R-CNN) was used to detect the category and size of the waste equipment in the images. The recognition and classification accuracy of the selected e-waste categories ranged from 90 to 97%. After the size and category of the waste is automatically recognized and classified from the uploaded images, e-waste collection companies can prepare a collection plan by assigning a sufficient number of vehicles and payload capacity for a specific e-waste project.