Standard

Development of a method of detection and classification of waste objects on a conveyor for a robotic sorting system. / Seredkin, A. V.; Tokarev, M. P.; Plohih, I. A. et al.

In: Journal of Physics: Conference Series, Vol. 1359, No. 1, 012127, 21.11.2019.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Seredkin, AV, Tokarev, MP, Plohih, IA, Gobyzov, OA & Markovich, DM 2019, 'Development of a method of detection and classification of waste objects on a conveyor for a robotic sorting system', Journal of Physics: Conference Series, vol. 1359, no. 1, 012127. https://doi.org/10.1088/1742-6596/1359/1/012127

APA

Seredkin, A. V., Tokarev, M. P., Plohih, I. A., Gobyzov, O. A., & Markovich, D. M. (2019). Development of a method of detection and classification of waste objects on a conveyor for a robotic sorting system. Journal of Physics: Conference Series, 1359(1), [012127]. https://doi.org/10.1088/1742-6596/1359/1/012127

Vancouver

Seredkin AV, Tokarev MP, Plohih IA, Gobyzov OA, Markovich DM. Development of a method of detection and classification of waste objects on a conveyor for a robotic sorting system. Journal of Physics: Conference Series. 2019 Nov 21;1359(1):012127. doi: 10.1088/1742-6596/1359/1/012127

Author

Seredkin, A. V. ; Tokarev, M. P. ; Plohih, I. A. et al. / Development of a method of detection and classification of waste objects on a conveyor for a robotic sorting system. In: Journal of Physics: Conference Series. 2019 ; Vol. 1359, No. 1.

BibTeX

@article{64bcf01d66a3446693a712a4bcef1f8f,
title = "Development of a method of detection and classification of waste objects on a conveyor for a robotic sorting system",
abstract = "Currently used recycling technologies have limitations on the composition of recyclable waste, which makes them specialized. Thus, the preliminary sorting of municipal solid waste is a necessary step, increasing the efficiency of using municipal solid waste as a resource. To sort municipal solid waste we developed a method for detecting and classifying waste on a conveyor line using neural network image processing. Images from a camera are fed to a neural network input, which determines the position and type of detected objects. To train the neural network a database of more than 13,000 municipal solid waste images was created. Mean-Average Precision for the neural network model was 64%.",
author = "Seredkin, {A. V.} and Tokarev, {M. P.} and Plohih, {I. A.} and Gobyzov, {O. A.} and Markovich, {D. M.}",
year = "2019",
month = nov,
day = "21",
doi = "10.1088/1742-6596/1359/1/012127",
language = "English",
volume = "1359",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
publisher = "IOP Publishing Ltd.",
number = "1",
note = "4th All-Russian Scientific Conference Thermophysics and Physical Hydrodynamics with the School for Young Scientists, TPH 2019 ; Conference date: 15-09-2019 Through 22-09-2019",

}

RIS

TY - JOUR

T1 - Development of a method of detection and classification of waste objects on a conveyor for a robotic sorting system

AU - Seredkin, A. V.

AU - Tokarev, M. P.

AU - Plohih, I. A.

AU - Gobyzov, O. A.

AU - Markovich, D. M.

PY - 2019/11/21

Y1 - 2019/11/21

N2 - Currently used recycling technologies have limitations on the composition of recyclable waste, which makes them specialized. Thus, the preliminary sorting of municipal solid waste is a necessary step, increasing the efficiency of using municipal solid waste as a resource. To sort municipal solid waste we developed a method for detecting and classifying waste on a conveyor line using neural network image processing. Images from a camera are fed to a neural network input, which determines the position and type of detected objects. To train the neural network a database of more than 13,000 municipal solid waste images was created. Mean-Average Precision for the neural network model was 64%.

AB - Currently used recycling technologies have limitations on the composition of recyclable waste, which makes them specialized. Thus, the preliminary sorting of municipal solid waste is a necessary step, increasing the efficiency of using municipal solid waste as a resource. To sort municipal solid waste we developed a method for detecting and classifying waste on a conveyor line using neural network image processing. Images from a camera are fed to a neural network input, which determines the position and type of detected objects. To train the neural network a database of more than 13,000 municipal solid waste images was created. Mean-Average Precision for the neural network model was 64%.

UR - http://www.scopus.com/inward/record.url?scp=85076469169&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/1359/1/012127

DO - 10.1088/1742-6596/1359/1/012127

M3 - Conference article

AN - SCOPUS:85076469169

VL - 1359

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012127

T2 - 4th All-Russian Scientific Conference Thermophysics and Physical Hydrodynamics with the School for Young Scientists, TPH 2019

Y2 - 15 September 2019 through 22 September 2019

ER -

ID: 22992554