Automatic Image-Based Waste Classification

Ruíz, Victoria; Sánchez, Ángel Mediavilla; Vélez, José Francisco; Raducanu, Bogdan

doi:10.1007/978-3-030-19651-6_41

Cited by 93 publications

(44 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To this aim, we employed ResNet pretrained on a large-scale ImageNet dataset [44] to extract object-level features from the input images. Our choice of the deep model for feature extraction is motivated by some recent works on the both applications [5], [34], [38], [45]. It is important to mention that the feature extraction part is independent of the AL and FL parts so it is expected that the choice of the model used for feature extraction will not have much impact on the overall analysis and insights of the ALbased FL.…”

Section: A Feature Extractionmentioning

confidence: 99%

Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification

et al. 2020

View full text Add to dashboard Cite

The feasibility of Federated Learning (FL) is highly dependent on the training and inference capabilities of local models, which are subject to the availability of meaningful and annotated data. The availability of such data is in turn contingent on the tedious and time-consuming annotation job that typically requires the manual analysis of training samples. Active Learning (AL) provides an alternative solution allowing a Machine Learning (ML) model to automatically choose and label the data from which it learns without involving manual inspection of each training sample. In this work, we explore how FL can benefit from unlabelled data available at each participating client using AL. To this aim, we propose an AL-based FL framework by employing and evaluating several AL methods in two different application domains. Through an extensive experimentation setup, we show that AL is equally useful in federated and centralized learning by achieving comparable results with manually labeled data using fewer samples without involving human annotators in collecting training data. We also demonstrated that the proposed method is dataset/application independent by evaluating the proposed method in two interesting applications, namely natural disaster analysis and waste classification, having different properties and challenges. Promising results are obtained on both applications resulting in comparable results against the best-case scenario where each sample is manually analyzed and annotated (Baseline 1), and improvement of 3.1% and 4% with best methods respectively over the training sets with irrelevant images on natural disaster and waste classification datasets (Baseline 2).

show abstract

Section: A Feature Extractionmentioning

confidence: 99%

Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification

et al. 2020

View full text Add to dashboard Cite

show abstract

“…According to Table 7, WasNet performs best on the public dataset TrashNet. [28] Augmented data on train R-CNN 68.30% Kennedy [29] OscarNet(based on VGG-19 pretrained) 88.42% Ruiz et al [30] Inception-ResNet model 88.66% Our WasNet(pretrained) 96.10%…”

Section: Comparisonsmentioning

confidence: 99%

WasNet: A Neural Network-Based Garbage Collection Management System

Yang

2020

IEEE Access

View full text Add to dashboard Cite

In response to the increasing pollution caused by unseparated garbage, classification systems for garbage separation have become very popular. First, we constructed a complex data augmentation combination for model training. Second, we designed a novel lightweight neural network garbage classification system called WasNet. This proposed network's 1.5 million parameters on the ImageNet dataset are onehalf of mainstream neural networks, while at 3 million floating point operations per second (FLOPs) it is one third of mainstream neural networks that have obtained the best performance among known lightweight neural networks. The accuracy on the ImageNet data set is 64.5%, on the Garbage Classification dataset it is 82.5%, and on the TrashNet dataset it is 96.10%. Furthermore, we transplanted the model to the hardware platform and assembled an intelligent trash can; we developed a garbage recognition application to facilitate users to directly identify and receive platform information; we built a visualization and decision support platform to help managers monitor traffic in real time. We combined the intelligent trash can, application, visualization and decision-making platform into a system, which is the most complete and effective system among the known research works. The results of the test we conducted on our platform using our extended dataset showed that our scheme is very reliable. At the same time, we also open source our extended datasets for use by other researchers.

show abstract

“…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%

“…Then, Ruiz et al [25] evaluated the use of several CNN models including VGG, Inception and ResNet for the automatic trash classification. In this study, the authors used 80% of Trashnet dataset for training, 10% for validation and the remaining 10% for testing.…”

Section: Related Workmentioning

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%

See 1 more Smart Citation

A Novel Framework for Trash Classification Using Deep Transfer Learning

Sơn

et al. 2019

IEEE Access

150

View full text Add to dashboard Cite

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.

show abstract

Automatic Image-Based Waste Classification

Cited by 93 publications

References 7 publications

Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification

Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification

WasNet: A Neural Network-Based Garbage Collection Management System

A Novel Framework for Trash Classification Using Deep Transfer Learning

Contact Info

Product

Resources

About