ThanosNet: A Novel Trash Classification Method Using Metadata

Sun, Alan; Xiao, Harry

doi:10.1109/bigdata50022.2020.9378287

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…The proposed system, DNN-TC, improved upon the ResNet model and achieved 94% accuracy in classifying the Trashnet dataset [5]. The authors of [6] suggested using location-and time-based traffic intensity to im-prove image classification They developed the ThanosNet model to classify five types of garbage in images-paper, tetra pak, landfill, plastic, and cans-which proved effective in classifying the ISBNet dataset. ScrapNet [7], MobileNet [8], SqueezeNet [9], PublicGarbageNet [10], and WasNet [11], similar to ThanosNet, are also deep learning models for classifying garbage with high accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Deep Trash Classification Model on Raspberry Pi 4

Tran¹,

Huynh²,

Le³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Environmental pollution has had substantial impacts on human life, and trash is one of the main sources of such pollution in most countries. Trash classification from a collection of trash images can limit the overloading of garbage disposal systems and efficiently promote recycling activities; thus, development of such a classification system is topical and urgent. This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time. An image dataset is first augmented to enhance the images before classifying them as either inorganic or organic trash. The deep learning-based ResNet-50 model, an improved version of the ResNet model, is used to classify trash from the dataset of trash images. The experimental results, which are tested both on the dataset and in real time, show that ResNet-50 had an average accuracy of 96%, higher than that of related models. Moreover, integrating the classification module into a Raspberry Pi computer, which controlled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste, created a complete trash classification system. This proves the efficiency and high applicability of the proposed system.

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Section: Related Workmentioning

confidence: 99%

A Deep Trash Classification Model on Raspberry Pi 4

Tran¹,

Huynh²,

Le³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…This is done by determining how well an object detector was educated using a mixture of actual and simulated trash image. In [79], the authors focus on the classification of garbage using metadata and evaluated the strategy using multiple deep learning algorithms such as VGG16, ResNet50, and DenseNet169 to compare it with the recently developed model ThanosNet, which achieved an accuracy of 94%. A lot of more research focuses on trash image classification from different devices such as in [80] for robotics, and those purely works with CNN with low accuracy such as in [81] and [82] using different benchmark datasets.…”

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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“…With the speedy advance of deep learning models [25][26][27][28], many studies investigated deep learning models for classification for various applications [28][29][30]. Lately, the authors in [25] presented a smoke concentration prediction neural network.…”

Section: Introductionmentioning

confidence: 99%

“…The abundant semantic data needed by intelligent encoderdecoder networks are used to compute smoke concentration from fire videos [26]. In [27], the authors used an energy-efficient neural network for fire and flame identification, and semantic considerate computation of the fire scenario. The authors in [28] joined the pixel and object-level prominent neural architectures to mine the smoke feature maps utilizing video frame sequence.…”

Section: Introductionmentioning

confidence: 99%

A Deep Real-Time Fire Prediction Parallel D-CNN Model on燯DOO燘OLT燰8

Alharbi¹,

Mahmoud²

2022

Computers, Materials &Amp; Continua

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Hazardous incidences have significant influences on human life, and fire is one of the foremost causes of such hazard in most nations. Fire prediction and classification model from a set of fire images can decrease the risk of losing human lives and assets. Timely promotion of fire emergency can be of great aid. Therefore, construction of these prediction models is relevant and critical. This article proposes an operative fire prediction model that depends on a prediction unit embedded in the processor UDOO BOLT V8 hardware to predict fires in real time. A fire image database is improved to enhance the images quality prior to classify them as either fire or nonfire. Our proposed deep learning-based Very Deep Convolutional Networks Visual Geometry Group (VGG-16) model (Parallel VGG-16) is an enhanced version of the VGG-16 model, by incorporating parallel convolution layers and a fusion module for better accuracy. The experimental results validate the performance of the Parallel VGG-16 which achieves an accuracy of 97%, compared to the compared state-of-the-art models. Moreover, we integrate the prediction module into a UDOO BOLT V8 computer, which precisely controlled the fire alarm so that it can cautious people from fire in real time.In this paper we propose a complete fire prediction model using a camera and the UDOO BOLT V8 embedded system. Our experiments validate the effectiveness and applicability of the proposed fire model.

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ThanosNet: A Novel Trash Classification Method Using Metadata

Cited by 7 publications

References 7 publications

A Deep Trash Classification Model on Raspberry Pi 4

A Deep Trash Classification Model on Raspberry Pi 4

A Survey on Waste Detection and Classification Using Deep Learning

A Deep Real-Time Fire Prediction Parallel D-CNN Model on燯DOO燘OLT燰8

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