A review of hyperspectral imaging-based plastic waste detection state-of-the-arts

Plastic waste issues emerged from the build-up of plastics that negatively impacts the environment. As a result, plastic waste detection is proposed in many research studies to tackle the problems. Therefore, this paper aims to review hyperspectral imaging techniques and machine learning in plastic waste detection. Hyperspectral imaging techniques are found to be effective in detecting plastic waste and microplastics as they were able to capture plastic reflectance spectral by using the near-infrared sensor. However, the review also shows that hyperspectral imaging techniques were less efficient in capturing the electromagnetic spectrum of black plastics due to carbon-black absorption properties. Carbon-black strongly absorbs light in the ultraviolet and infrared spectral range of the electromagnetic spectrum, therefore not detected by the near-infrared sensor. This paper also reviews how machine learning can alternatively detect and sort all types of waste, including plastics. Multiple studies show that the machine learning model achieved good accuracy in detecting all types of plastics based on the waste dataset. Finally, it can be seen that the spectral information of plastic can be used as feature extraction for machine learning models for better plastic detection. It is hoped that this study will contribute to more systematic research on the same topic

Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image

Sorting waste has always been an important part of managing waste. The primary issue with the waste sorting process has been the discomfort caused by prolonged contact with waste odor. A machinelearning method for identifying waste types was created to address this issue. The study’s goal was to create machine learning to solve waste management challenges by applying the most accurate categorization model available. The research approach was the quantitative analysis of the classification model accuracy. The Kaggle dataset was used to collect and curate data, which was subsequently preprocessed using the morphology approach. Based on picture sources, the data was trained and used to classify waste. The Support Vector Machine model was used in this investigation and feature extraction via the Convolutional Neural Network. The results showed that the system categorized waste successfully, with an accuracy of 99.30% and a loss of 2.47% across all categories. According to the findings of this study, SVM combined with morphological image processing functioned as a strong classification model, with a remarkable accuracy rate of 99.30%. This study’s outcomes contributed to waste management by giving an efficient and dependable waste classification solution compared to many previous studies

An Improved ResNet-50 for Garbage Image Classification

In order to solve the classification model\u27s shortcomings, this study suggests a new trash classification model that is generated by altering the structure of the ResNet-50 network. The improvement is divided into two sections. The first section is to change the residual block. To filter the input features, the attention module is inserted into the residual block. Simultaneously, the downsampling process in the residual block is changed to decrease information loss. The second section is multi-scale feature fusion. To optimize feature usage, horizontal and vertical multi-scale feature fusion is integrated to the primary network structure. Because of the filtering and reuse of image features, the enhanced model can achieve higher classification performance than existing models for small data sets with few samples. The experimental results show that the modified model outperforms the original ResNet-50 model on the TrashNet dataset by 7.62% and is more robust. In the meanwhile, our model is more accurate than other advanced methods

Analysis of Biodegradable and Non-Biodegradable Materials Using Selected Deep Learning Algorithms

It is possible to divide the materials used in the world into recyclable and nonrecyclable. Biodegradable materials contain elements naturally degraded by microorganisms such as foods, plants, fruits, etc. Waste from this material can be processed into compost. non-biodegradable materials include materials that do not naturally decompose, such as plastics, metals, inorganic elements, etc. Waste from this material can only be reused by converting it into new materials. In this study, the classification of biodegradable and non-biodegradable materials was done using deep learning methods. Convolutional Neural Network (CNN) performs steps such as preprocessing and feature extraction in classification. 5430 images were used for the dataset. 70% of this dataset was used as training data, 15% as validation data, and 15% as test data. Of the Deep Learning methods, the pre-trained neural networks AlexNet, ShuffleNet, SqueezeNet, and GoogleNet were used. For each algorithm, the performances were evaluated by classifying them as biodegradable and non-biodegradable. With this study, we can identify, track, sort, and process waste materials by classifying materials

Systematic Literature Review of Waste Classification Using Machine Learning

The development of the global economy has caused people's living standards to increase and the production of domestic waste has also increased from year to year. The population of big cities that have limited environmental carrying capacity, causing the waste problem requires serious handling. Manual waste sorting is hazardous to health and wastes time, money and effort. If waste is not handled properly, environmental problems will increase in the long run. Machine learning works by combining features such as textures and colors to complement junk image recognition. Today's machine learning technology continues to develop, not only methods, types of waste, and features but also identify and analyze datasets used in waste management by gathering all scientific evidence. Collecting existing research and then identifying, assessing, and interpreting requires a systematic literature review. Until the end of 2021, the research topic of waste classification using machine learning was found with various types of waste, algorithms, datasets, and others. However, the dataset used by the algorithm in image recognition is relatively single, the types of garbage classified and the relative accuracy results can still be improved

Identification of residues deposited outside of the deposition equipment, using video analytics

In areas where waste production is excessive, sometimes improper deposition occurs around the garbage equipment, requiring more effort from the waste collection teams. In this dissertation an image recognition system is proposed for the detection and classification of waste outside the existing waste disposal equipment. The main motivation is to facilitate the work of waste collection in the city of Lisbon, which is done by the teams of the Lisbon Waste Collection Centers. In order to help the waste collection planning, the collection team inspectors in partnership with the Lisbon City Council created a repository with several datasets, which they named, 'LxDataLab'. The collected images go through the pre-processing process and finally are submitted to waste detection and classification, through deep learning networks. In this sense, a classification and identification method using neural networks for image analysis is proposed: the first approach consisted in training a deep learning convolutional neural network (CNN) specifically developed to classify residues; in a second approach a CNN was trained using a pre-trained MobileNetV2 model, which only the last layer was trained. The training in this approach was faster compared to the previous approach, as were the performance values in detecting the class and the amount of residues in the images. The hit rate for the classification of the selected debris varied between 80%, for test set. After the detection and classification of the residues in the images are recognized, annotations are generated on the images.Nas áreas onde a produção de resíduos é excessiva, por vezes ocorre a deposição indevida em torno dos equipamentos de deposição de lixo, exigindo mais esforço por parte das equipas de recolha destes resíduos. Nesta dissertação é proposto um sistema de reconhecimento de imagem para a deteção e classificação de resíduos fora dos equipamentos de deposição existentes para o mesmo. A principal motivação é facilitar o trabalho de recolha dos resíduos na cidade de Lisboa. De forma a possibilitar o desenvolvimento de algoritmos que possam vir a ser úteis na automatização de tarefas em diferentes áreas de intervenção, a Câmara Municipal de Lisboa criou um repositório, denominado ‘LxDataLab’, contendo vários conjuntos de dados. Estes dados, por sua vez são submetidos a um processo pré-processamento e por fim são submetidas para deteção e classificação dos resíduos. Assim é proposto um método de classificação e identificação de resíduos utilizando redes neuronais para análise de imagens: a primeira abordagem consistiu no treino de uma rede neuronal convolucional de aprendizagem profunda (CNN) desenvolvida especificamente para classificar resíduos; numa segunda abordagem foi treinada uma CNN utilizando um modelo pré-treinado MobileNetV2. Nesta última abordagem, o treino foi mais rápido em relação à abordagem anterior, e o desempenho na deteção da classe e da quantidade de resíduos nas imagens foi superior. A taxa de acerto para as classes de resíduos selecionadas variou nos 80% para o conjunto de teste. Após a deteção e classificação dos resíduos nas imagens são geradas anotações nas mesmas

An Overview of Carbon Footprint Mitigation Strategies. Machine Learning for Societal Improvement, Modernization, and Progress

Among the most pressing issues in the world today is the impact of globalization and energy consumption on the environment. Despite the growing regulatory framework to prevent ecological degradation, sustainability continues to be a problem. Machine learning can help with the transition toward a net-zero carbon society. Substantial work has been done in this direction. Changing electrical systems, transportation, buildings, industry, and land use are all necessary to reduce greenhouse gas emissions. Considering the carbon footprint aspect of sustainability, this chapter provides a detailed overview of how machine learning can be applied to forge a path to ecological sustainability in each of these areas. The chapter highlights how various machine learning algorithms are used to increase the use of renewable energy, efficient transportation, and waste management systems to reduce the carbon footprint. The authors summarize the findings from the current research literature and conclude by providing a few future directions

Computer Vision For Recycling

Americans recycle 32.1 percent of all the waste they create, as confirmed by the latest report from the Environmental Protection Agency. However, the underlying issue that remains is that most Americans are not equipped with the knowledge of the correct methods of recycling – and the deficiency of that knowledge is the greatest quandary to ensuring that the country is “green” and environment friendly. A survey of two thousand American citizens revealed that 62 percent of them worry that this inadequate knowledge is causing them to recycle incorrectly. The aim of this research is to develop an Android App, where with the use of one’s smartphone camera, a person can capture an image of an item that they wish to recycle and the output displayed on the app will be either plastic, glass, metal or garbage(unknown). The first component of the research investigates whether a usable model can be built that can be used to robustly identify recyclable objects. A deep convolutional neural network (CNN) is built in Python and trained on a labeled data set of a thousand product images from various perspectives, to determine whether the object that is to be recycled is composed of plastic, metal or glass. In order to provide the most efficient approach, I experimented on well-known deep convolutional neural network architectures. By implementing transfer learning and fine tuning to the pre-trained models with a common data augmentation strategy ResNet101V2 model provided the best result with 82% test accuracy. A larger data set is required to reduce overfitting and increase the accuracy. The chief purpose of this project is to develop an application based on a deep learning model that aids users to correctly identify the nature of objects that they deem recyclable and to widen the scope of healthy recycling - of household and domestic goods, which is a tiny but indispensable and effective individual step that needs to be taken in order to combat pollution and in the long run, prevent climate change while there is still time to do so