SpotGarbage

Mittal, Gaurav; Yagnik, Kaushal B.; Garg, Mohit; Krishnan, Narayanan C.

doi:10.1145/2971648.2971731

Cited by 154 publications

(26 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An Auto‐Trash device has been used to sort compostable items and recyclable items, which is built by the TensorFlow AI engine. A smartphone app called SpotGarbage has been developed to identify garbage in pictures taken with the phone camera. The app has a deep architecture of CNN and based on the AlexNet model.…”

Section: Related Workmentioning

confidence: 99%

“…In the latest studies and applications, an Support Vector Machine (SVM)‐scale‐invariant feature transform (SIFT) method achieves only 63% classification accuracy for a manually collected single waste image dataset, while the Convolution Neural Network (CNN)+SVM method using ResNet50 as a pretrained model achieves 87% classification accuracy. Garbage sorting devices and mobile applications that have been put into use can achieve about 90% classification accuracy …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A combination model based on transfer learning for waste classification

Huang

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

Summary The increasing amount of solid waste is becoming a significant problem that needs to be addressed urgently. The reliable and accurate classification method is a crucial step in waste disposal because different types of wastes have different disposal ways. The existing waste classification models driven by deep learning are not easy to achieve accurate results and still need to be improved due to the various architecture networks adopted. Their performance on different datasets is varied, and there is also a lack of specific large‐scale datasets for training. We propose a new combination classification model based on three pretrained CNN models (VGG19, DenseNet169, and NASNetLarge) for processing the ImageNet database and achieve high classification accuracy. In our proposed model, the transfer learning model based on each pretrained model is constructed as a candidate classifier, and the optimal output of three candidate classifiers is selected as the final classification result. The experiments based on two waste image datasets demonstrate that the proposed model achieves 96.5% and 94% classification accuracy and outperforms several counterpart methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A combination model based on transfer learning for waste classification

Huang

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Because heart disease has the characteristic of occurring suddenly, it is necessary to use deep learning in mobile devices for real-time monitoring. There are many studies which use mobile applications to perform task, such as using CNN to identify garbage in images [21]. However, experiments show that the resources consumption of these applications are still very high.…”

Section: Related Workmentioning

confidence: 99%

Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Wireless and mobile health applications promote the development of smart healthcare. Effective diagnosis and feedbacks of remote health data pose significant challenges due to streaming data, high noise, network latency and user privacy. Therefore, we explore efficient edge and cloud design to maintain electrocardiogram classification performance while reducing the communication cost. These contributions include: (1) We introduce a hybrid smart medical architecture named edge convolutional neural networks (EdgeCNN) that balances the capability of edge and cloud computing to address the issue for agile learning of healthcare data from IoT devices. (2) We present an effective deep learning model for electrocardiogram (ECG) inference, which can be deployed to run on edge smart devices for low-latency diagnosis. (3) We design a data enhancement method for ECG based on deep convolutional generative adversarial network to expand ECG data volume. (4) We carried out experiments on two representative datasets to evaluate the effectiveness of the deep learning model of ECG classification based on EdgeCNN. EdgeCNN shows superior to traditional cloud medical systems in terms of network Input/Output (I/O) pressure, architecture cost and system high availability. The deep learning model not only ensures high diagnostic accuracy, but also has advantages in aspect of inference time, storage, running memory and power consumption.

show abstract

“…Seperti sampah kardus, plastik, kertas, logam dan kaca merupakan bahan yang dapat didaur ulang untuk memperoleh barang olahan yang baru. Selain itu daur ulang juga menghasilkan keuntungan yang bisa memberikan ekonomi menjadi baik [17].…”

unclassified

“…Hasil dari penelitian menunjukkan bahwa sistem SVM menghasilkan kecepatan pengenalan yang lebih cepat dengan tingkat pengenalan yang baik [11]. Menurut [17] yang melakukan penelitian tentang klasifikasi sampah daur ulang menggunakan metode support vector machine (SVM) dengan fitur scale-invariant feature transform (SIFT) dan convolutional neural network (CNN). Dengan menggunakan dataset berisi citra sampah yang dapat daur ulang dibagi menjadi enam kelas masing-masing sekitar 400-500 citra.…”

unclassified

Klasifikasi Sampah Daur Ulang Menggunakan Support Vector Machine Dengan Fitur Local Binary Pattern

Leonardo¹,

Yohannes²,

Hartati³

2020

algoritme

View full text Add to dashboard Cite

Garbage is one of the problems that always arise in Indonesia and even in the world. Increasingly, the production of waste is increased along with the increase in population and consumption. Therefore, need a prevention to stop wasting or producing garbage through recycle. This research do garbage recycle classification of cardboard, glass, metal, paper and plastic by using Local Binary Pattern (LBP) texture feature extraction methode and Support Vector Machine (SVM) as classification methode. For examination technic and dataset distribution is using K-Fold Cross Validation methode type Leave One Out (LOO). From examination result had been done were using fold 5 until fold 10. Polynomial kernel get highest accuracy result from every fold used with mean point 87.82%. Based on SVM classification examination result whether linear kernel, polynomial nor gaussian by using fold 5 until fold 10. The best accuracy point for cardboard garbage is 96.01%. For glass garbage, the best accuracy point is 90.62%. Then, metal garbage get the best accuracy point 89.72%. While paper garbage with highest accuracy point 96.01%. And plastic garbage with highest accuracy point 87.64%.

show abstract

SpotGarbage

Cited by 154 publications

References 15 publications

A combination model based on transfer learning for waste classification

A combination model based on transfer learning for waste classification

Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks

Klasifikasi Sampah Daur Ulang Menggunakan Support Vector Machine Dengan Fitur Local Binary Pattern

Contact Info

Product

Resources

About