Clean Our City: An Automatic Urban Garbage Classification Algorithm Using Computer Vision and Transfer Learning Technologies

Chen, Yuke; Han, Wendi; Jin, Jizhi; Wang, Hao; Xing, Qiaochu; Zhang, Yiwen

doi:10.1088/1742-6596/1994/1/012022

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…The model creates different generators for training and validation sets and augments the data using the Keras ImageDataGenerator. For each classi cation task, it loads the underlying InceptionV3 model that has already been pre-trained on ImageNet [17], eliminates the top classi cation layer, and inserts custom layers. A GlobalAveragePooling2D layer, a Dropout layer [19] for regularisation, a dense hidden layer activated by ReLU, and 6.…”

Section: Inceptionv2mentioning

confidence: 99%

Comparative Study of ML Algorithms for Garbage Classification

Buchade,

Bhoite

2024

Preprint

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In today's world, the escalating waste crisis demands effective garbage classification strategies. As population growth and evolving needs contribute to unprecedented waste generation, repurposing items through recycling, reproduction, or reuse becomes imperative. Proper garbage classification is pivotal in realizing these goals. This paper presents a concise yet comprehensive comparative study of machine learning algorithms for garbage classification. The primary objectives include comparing the performance of MobileNetV2, InceptionV3, and ResNet in garbage classification and scrutinizing optimal algorithms employed by researchers. The dataset comprises six garbage classes: cardboard, metal, paper, plastic, glass, and trash. Through rigorous evaluation, insights into algorithmic performance are presented. MobileNetV2 stands out, achieving a remarkable 94.48% accuracy on the validation set with minimal loss. InceptionV3 and ResNet50 yield accuracies of 86.08% and 88.54%, respectively. This study not only contributes to garbage classification knowledge but also highlights the real-world potential of the top-performing algorithm. As we address waste management complexities, this research signifies a step toward implementing efficient and accurate garbage classification systems for a sustainable future.

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Section: Inceptionv2mentioning

confidence: 99%

Comparative Study of ML Algorithms for Garbage Classification

Buchade,

Bhoite

2024

Preprint

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“…In order to separately predict and obtain three predicted probability vectors, which are important components that affect the prediction performance by providing complementary information about the patterns to be classified, three cuttingedge CNNs-GoogleNet, ResNet-50, and MobileNetV2are first used as ingredient classifiers. The technique in the paper given by Chen et al, [20] is built on InceptionV3 networks, and it is tested using a sizable data set for trash categorization. The transfer learning method was used to divide the data set into 80% training sets, 10% validation sets, and 10% test sets.…”

Section: Related Workmentioning

confidence: 99%

Iweews: An Intelligent Waste Extractor for Efficient Waste Segregation by Using Deep Learning Technique

Bobde M,

Jothi,

Sharma

2023

IJEAST

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Waste segregation is a top priority in many countries across the world. Even in this day and age, many individuals are unable to discern between biodegradable, recyclable, and landfill waste. As a result, the world is currently dealing with a serious basic issue with garbage disposal. In this study, we use deep learning algorithms to handle the trash categorization problem. Organic waste, recyclable rubbish, and landfill garbage are the three major types of waste. A way to speed up the whole process is by doing waste sorting, which could be done by computer using image recognition. CNNs have gradually been used in a number of intelligent trash identification and recycling applications during the past few years (IWIR). Nevertheless, because there aren't any established standards or benchmarks for the datasets and models being utilised, current studies on IWIR are difficult to summarise because CNNs are still relatively new to environmental researchers.EfficientNet-B3 CV model which could be utilized in this scenario due to the more efficient architecture and comparable performance with others deep convolutional neural network. Deep learning has now made it possible to overcome one of the biggest obstacles to effective waste management and hence, this issue can be handled with utmost care.

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“…The findings of the tests model indicate that the suggested model has a greater level of accuracy in terms of both detection and classification in comparison to the original YOLOv5 model, and that it is also capable of meeting the actual application requirements in terms of its real-time performance. In a research by [73] makes a suggestion for an algorithm that is based on InceptionV3 networks and tests the model on a garbage classification dataset that is quite huge in scale. Transfer learning was used in the dataset, which was then segmented into training sets consisting of 80 %, validation sets consisting of 10 %, and test sets consisting of 10 %.…”

Section: B Waste Classificationmentioning

confidence: 99%

A Survey on Waste Detection and Classification Using Deep Learning

Abdu

Noor

2022

IEEE Access

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Waste or trash management is receiving increased attention for intelligent and sustainable development, particularly in developed and developing countries. The waste or trash management system comprises several related processes that carry out various complex functions. Recently, interest in deep learning (DL) has increased in providing alternative computational techniques for determining the solution to various waste or trash management problems. Researchers have concentrated on this domain, and as a result, significant research has been published, particularly in recent years. According to the literature, a few comprehensive surveys have been done on waste detection and classification. However, no study has investigated the application of DL to solve waste or trash management problems in various domains and highlight the available datasets for waste detection and classification in different domains. To this end, this survey contributes by reviewing various image classification and object detection models, and their applications in waste detection and classification problems, providing an analysis of waste detection and classification techniques with precise and organized representation and compiling over twenty benchmarked trash datasets. Also, we backed up the study with the challenges of existing methods and the future potential in this field. This will give researchers in this area a solid background and knowledge of the state-of-the-art deep learning models and insight into the research areas that can still be explored.

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Clean Our City: An Automatic Urban Garbage Classification Algorithm Using Computer Vision and Transfer Learning Technologies

Cited by 7 publications

References 8 publications

Comparative Study of ML Algorithms for Garbage Classification

Comparative Study of ML Algorithms for Garbage Classification

Iweews: An Intelligent Waste Extractor for Efficient Waste Segregation by Using Deep Learning Technique

A Survey on Waste Detection and Classification Using Deep Learning

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