Trash and Recycled Material Identification using Convolutional Neural Networks (CNN)

Sultana, Rumana; Adams, Robert D.; Yan, Yanjun; Yanik, Paul; Tanaka, Martín

doi:10.1109/southeastcon44009.2020.9249739

Cited by 16 publications

(9 citation statements)

References 3 publications

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“…For instance, the potential unstructured surrounding environment that garbage may lie in, or the fact that a robot operating robustly and efficiently in such a task, involves many as-pects of operations, such as object recognition, grasp pose estimation, grasp control algorithm, path planning, etc. Even though there is work to been done on garbage detection [9], [10], the only mobile manipulation robotic system that has developed a pick-up garbage method on the grass is the one presented by Bai et al [11]. In particular, a deep learning method is deployed to classify the waste on the grass (i.e., as waste or not) and a novel navigation algorithm is presented based on grass segmentation.…”

Section: A Related Workmentioning

confidence: 99%

Garbage Collection and Sorting with a Mobile Manipulator using Deep Learning and Whole-Body Control

Liu

Balatti

Ellis

et al. 2021

2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids)

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Domestic garbage management is an important aspect of a sustainable environment. This paper presents a novel garbage classification and localization system for grasping and placement in the correct recycling bin, integrated on a mobile manipulator. In particular, we first introduce and train a deep neural network (namely, GarbageNet) to detect different recyclable types of garbage. Secondly, we use a grasp localization method to identify a suitable grasp pose to pick the garbage from the ground. Finally, we perform grasping and sorting of the objects by the mobile robot through a whole-body control framework. We experimentally validate the method, both on visual RGB-D data and indoors on a real fullsize mobile manipulator for collection and recycling of garbage items placed on the ground.

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Section: A Related Workmentioning

confidence: 99%

Garbage Collection and Sorting with a Mobile Manipulator using Deep Learning and Whole-Body Control

Liu

Balatti

Ellis

et al. 2021

2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids)

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“…Adapun pada penelitian [8] CNN akan melatih dan menguji dataset, setiap masukan gambar akan melalui sekelompok convolution layer dengan nilai probabilitas antara 0 dan 1, Karena sifat proses konvolusi, maka CNN hanya dapat digunakan pada data yang memiliki struktur dua dimensi seperti citra dan suara, seperti yang ditunjukkan pada Gambar 4.…”

Section: Pendahuluanunclassified

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

Ramadhani¹,

Thohari

Kartiko³

et al. 2021

RESTI

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Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

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“…Penelitian ini menggunakan dataset yang digunakan pada penelitian [7], [9]. Dimana dataset ini diambil dari Gary Thung dan Mindy Yang's dataset berisi jenis sampah yang dikategorikan menjadi enam kelas diantaranya : cardboard 403 gambar, glass 501 gambar, metal 410 gambar, paper 594 gambar, plastic 482 gambar, dan trash 137 gambar.…”

Section: Datasetunclassified

“…Kenaikan accuracy terjadi sebesar 3% hingga 5% dari model tunggal, dengan performa accuracy akhir pada metode ensemble sebesar 96%. Sehingga, metode ensemble yang diterapkan mampu meningkatkan accuracy pada dua penelitian sebelumnya dengan dataset serupa yang memiliki accuracy masingmasing sebesar 93,6% dan 94,9%[7],[9] menjadi 96%. Seiring dengan peningkatan accuracy parameter lain juga mengalami peningkatan diantaranya precision, recall, dan f1-score dari masing-masing kelas.…”

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Garbage Classification Using Ensemble DenseNet169

Oktaviana

Azhar

2021

RESTI

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Garbage is a big problem for the sustainability of the environment, economy, and society, where the demand for waste increases along with the growth of society and its needs. Where in 2019 Indonesia was able to produce 66-67 million tons of waste, which is an increase from the previous year of 2 to 3 million tons of waste. Waste management efforts have been carried out by the government, including by making waste sorting regulations. This sorting is known as 3R (reduce, reuse, recycle), but most people do not sort their waste properly. In this study, a model was developed that can sort out 6 types of waste including: cardboard, glass, metal, paper, plastic, trash. The model was built using the transfer learning method with a pretrained model DenseNet169. Where the optimal results are shown for the classes that have been oversampling previously with an accuracy of 91%, an increase of 1% compared to the model that has an unbalanced data distribution. The next model optimization is done by applying the ensemble method to the four models that have been oversampled on the training dataset with the same architecture. This method shows an increase of 3% to 5% while the final accuracy on the test of dataset is 96%.

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Trash and Recycled Material Identification using Convolutional Neural Networks (CNN)

Cited by 16 publications

References 3 publications

Garbage Collection and Sorting with a Mobile Manipulator using Deep Learning and Whole-Body Control

Garbage Collection and Sorting with a Mobile Manipulator using Deep Learning and Whole-Body Control

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

Garbage Classification Using Ensemble DenseNet169

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