Classification of TrashNet Dataset Based on Deep Learning Models

Aral, Rahmi Arda; Keskin, Şeref Recep; Kaya, Mahmut; Haciömeroğlu, Murat

doi:10.1109/bigdata.2018.8622212

Cited by 141 publications

(61 citation statements)

References 6 publications

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“…In their work, they collected the TrashNet dataset of 2500 images of single pieces of waste. Based on their data, Bircanoğlu et al [ 25 ] and Aral et al [ 26 ] performed detailed comparisons among various deep architectures. Additionally, Awe et al [ 27 ] created a synthetic dataset for waste object detection based on Faster RCNN [ 16 ].…”

Section: Related Workmentioning

confidence: 99%

A Multi-Level Approach to Waste Object Segmentation

Wang

Cai

Liang

et al. 2020

Sensors

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We address the problem of localizing waste objects from a color image and an optional depth image, which is a key perception component for robotic interaction with such objects. Specifically, our method integrates the intensity and depth information at multiple levels of spatial granularity. Firstly, a scene-level deep network produces an initial coarse segmentation, based on which we select a few potential object regions to zoom in and perform fine segmentation. The results of the above steps are further integrated into a densely connected conditional random field that learns to respect the appearance, depth, and spatial affinities with pixel-level accuracy. In addition, we create a new RGBD waste object segmentation dataset, MJU-Waste, that is made public to facilitate future research in this area. The efficacy of our method is validated on both MJU-Waste and the Trash Annotation in Context (TACO) dataset.

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Section: Related Workmentioning

confidence: 99%

A Multi-Level Approach to Waste Object Segmentation

Wang

Cai

Liang

et al. 2020

Sensors

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“…The detail descriptions of this dataset will be presented in the next section. Several studies regarding trash classification problem [23]- [25] utilizing Trashnet dataset for evaluating their proposed approaches which are summarized as follows.…”

Section: Related Workmentioning

confidence: 99%

“…Firstly, Aral et al [23] utilized the transfer learning models originated from several well-known CNN models for image classification including Densenet121, DenseNet169, Incep-tionResnetV2, MobileNet, and Xception to classify trash on Trashnet dataset. In their experiments, the authors used 70% of Trashnet dataset for training, 13% for validation and 17% for testing.…”

Section: Related Workmentioning

confidence: 99%

“…This study utilizes state-of-the-art methods for trash classification including Densenet121_Aral [23], RecycleNet [24] and ResNet_Ruiz [25] as well as a famous deep learning model for image classification namely ResNext-101 [26] to compare with the proposed approach for trash classification. Specifically, three methods including Densenet121_Aral, RecycleNet, and ResNet_Ruiz were implemented based on the same configurations which were described in their works.…”

Section: B Experimental Settingmentioning

confidence: 99%

“…This model uses a CNN-based algorithm to extract image features and the multilayer perceptron (MLP) method to consolidate image features and other feature information to classify wastes as recyclable or the others. In 2016, Yang and Thung [22] released Trashnet dataset which is often used to evaluate trash classification models [23]- [25]. However, these models are not effective in cases where the class contains many objects.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Framework for Trash Classification Using Deep Transfer Learning

Sơn

et al. 2019

IEEE Access

155

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Nowadays, society is growing and crowded, the construction of automatic smart waste sorter machine utilizing the intelligent sensors is important and necessary. To build this system, trash classification from trash images is an important issue in computer vision to be addressed for integrating into sensors. Therefore, this study proposes a robust model using deep neural networks to classify trash automatically which can be applied in smart waste sorter machines. Firstly, we collect the VN-trash dataset that consists of 5904 images belonging to three different classes including Organic, Inorganic and Medical wastes from Vietnam. Next, this study develops a deep neural network model for trash classification named DNN-TC which is an improvement of ResNext model to improve the predictive performance. Finally, the experiments are conducted to compare the performances of DNN-TC and the state-of-the-art methods for trash classification on VN-trash dataset as well as Trashnet dataset to show the effectiveness of the proposed model. The experimental results indicate that DNN-TC yields 94% and 98% in terms of accuracy for Trashnet and VN-trash datasets respectively and thus it outperforms the state-of-the-art methods for trash classification on both experimental datasets. INDEX TERMS Trash classification, computer vision, deep neural networks.

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