Deep Learning based Smart Garbage Classifier for Effective Waste Management

Sidharth, R; Rohit, P; Vishagan, S; Karthika, R.; Ganesan, M.

doi:10.1109/icces48766.2020.9137938

Cited by 19 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By training the CNNs on medical waste images, the system can accurately classify medical waste types like infectious, hazardous, pathological, pharmaceutical, and radioactive waste. This approach aids in the safe disposal and segregation of medical waste, minimizing health risks and environmental contamination [47,48]. Additionally, the LSTM CNN can forecast the volume of medical waste generated over time, enabling effective resource allocation and waste management strategies.…”

Section: Deep Learning and Neural Network In Segregation And Treatmen...mentioning

confidence: 99%

Modernized Management of Biomedical Waste Assisted with Artificial Intelligence

Sarkar,

Dey,

Malik

2023

Int J Biomed Clin Anal

View full text Add to dashboard Cite

Biomedical waste can lead to severe environmental pollution and pose public health risks if not properly handled or disposed of. The efficient management of biomedical waste poses a significant challenge to healthcare facilities, environmental agencies, and regulatory bodies. Traditional management methods often fall short of efficient handling of biomedical waste due to its enormous quantity, diverse, and complex nature. In recent years, different approaches employing Artificial Intelligence (AI) techniques have been introduced and have shown promising potential in biomedical waste management. Wireless detection and IoT methods have enabled the monitoring of waste bins, predictions for the amount of waste, and optimization of the performance of waste processing facilities. This review paper aims to explore the application of AI through machine learning and deep learning models in optimizing the collection, segregation, transportation, disposal, and monitoring processes, which leads to improved resource allocation with risk mitigation of biomedical waste along with prediction, and decision-making using AI algorithms.

show abstract

Section: Deep Learning and Neural Network In Segregation And Treatmen...mentioning

confidence: 99%

Modernized Management of Biomedical Waste Assisted with Artificial Intelligence

Sarkar,

Dey,

Malik

2023

Int J Biomed Clin Anal

View full text Add to dashboard Cite

show abstract

“…In six-class systems [49][50][51][52] researchers focus on recyclable waste classes: paper, metal, glass, cardboard, plastic, and trash. In four-class systems [53][54][55][56][57] waste classes are wet waste, i.e., probably kitchen waste, dry garbage, recyclable and hazardous garbage. A ResNet-5013 model and SVM-based intelligent system was proposed in [58].…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Waste Management and Classification Systems Using Cutting Edge Approach

2022

View full text Add to dashboard Cite

With a rapid increase in population, many problems arise in relation to waste dumps. These emits hazardous gases, which have negative effects on human health. The main issue is the domestic solid waste collection, management, and classification. According to studies, in America, nearly 75% of waste can be recycled, but there is a lack of a proper real-time waste-segregating mechanism, due to which only 30% of waste is being recycled at present. To maintain a clean and green environment, we need a smart waste management and classification system. To tackle the above-highlighted issue, we propose a real-time smart waste management and classification mechanism using a cutting-edge approach (SWMACM-CA). It uses the Internet of Things (IoT), deep learning (DL), and cutting-edge techniques to classify and segregate waste items in a dump area. Moreover, we propose a waste grid segmentation mechanism, which maps the pile at the waste yard into grid-like segments. A camera captures the waste yard image and sends it to an edge node to create a waste grid. The grid cell image segments act as a test image for trained deep learning, which can make a particular waste item prediction. The deep-learning algorithm used for this specific project is Visual Geometry Group with 16 layers (VGG16). The model is trained on a cloud server deployed at the edge node to minimize overall latency. By adopting hybrid and decentralized computing models, we can reduce the delay factor and efficiently use computational resources. The overall accuracy of the trained algorithm is over 90%, which is quite effective. Therefore, our proposed (SWMACM-CA) system provides more accurate results than existing state-of-the-art solutions, which is the core objective of this work.

show abstract

“…The first problem is that due to the different classification standards for different countries and regions, the number of public datasets is small, and the image quality in the datasets varies. For example, the input image dataset for deep learning to train a network by Sidharth et al consists of only 2077 image samples of four categories [15]. The second problem is that from the actual application effect of the current garbage classification algorithm, some algorithms or CNN neural network applications do not give very clear actual test results, and the robustness and generalization performance of the model cannot be well verified.…”

Section: Development and Analysismentioning

confidence: 99%

“…The TrashNet dataset has been widely used in the field of deep-learning garbage classification since its publication to evaluate the classification effect of the designed neural network after training. The literature [15,16,19,20] used this dataset to design garbage classification. By observing image samples of the TrashNet dataset, it is found that the background of most of the images is relatively clean, but there is still the problem of inaccurate selection of individual image samples.…”

Section: Trashnet Datasetmentioning

confidence: 99%

A Garbage Classification Method Based on a Small Convolution Neural Network

et al. 2022

View full text Add to dashboard Cite

To improve the efficiency of social garbage classification, a garbage classification method based on a small convolutional neural network (CNN) is proposed in this paper. For low accuracy caused by light and shadow interference, an adaptive image-brightening algorithm is developed to average the brightness of the background in the image preprocessing stage, and a threshold replacement method is used to reduce shadow noise. Then, the Canny operator is used to assist in cropping the blank background in the image. For debugging low efficiency caused by the complex network, the neural network is optimized based on the MLH-CNN model to make its results simpler and equally efficient. Experimental results show the preprocessing in this study can improve the accuracy of model garbage classification. The CNN model in this study can achieve an accuracy of 96.77% on the self-built dataset and 93.72% on the TrashNet dataset, which is higher than the 92.6% accuracy of the MLC-CNN model. The network optimizer can also enhance the classification ability of the network model using the Adamax optimization algorithm based on Adam variants. In this paper, the network model derived from training is combined with the host computer software to design a garbage detection page so the model has a wider range of uses, which has a good effect on promoting the development of social environmental protection and improving residents’ awareness of environmental protection.

show abstract

Deep Learning based Smart Garbage Classifier for Effective Waste Management

Cited by 19 publications

References 8 publications

Modernized Management of Biomedical Waste Assisted with Artificial Intelligence

Modernized Management of Biomedical Waste Assisted with Artificial Intelligence

Smart Waste Management and Classification Systems Using Cutting Edge Approach

A Garbage Classification Method Based on a Small Convolution Neural Network

Contact Info

Product

Resources

About