Image Processing for Picking Task of Random Ordered PET Drinking Bottles

In this research, six brands of soft drinks are decided to be picked up by a robot with a monocular Red Green Blue (RGB) camera. The drinking bottles need to be located and classified with brands before being picked up. The Mask Regional Convolutional Neural Network (R-CNN), a mask generation networ...
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Gespeichert in:

Autor*in:	Chen Zhu [verfasserIn] Takafumi Matsumaru [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Image processing robotics picking deep learning COCO dataset

Übergeordnetes Werk:	In: Journal of Robotics, Networking and Artificial Life (JRNAL) - Atlantis Press, 2017, 6(2019), 1
Übergeordnetes Werk:	volume:6 ; year:2019 ; number:1

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.2991/jrnal.k.190531.008

Katalog-ID:	DOAJ032696574

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spelling	10.2991/jrnal.k.190531.008 doi (DE-627)DOAJ032696574 (DE-599)DOAJ0ea523675d9f4713a78022839d0e36bf DE-627 ger DE-627 rakwb eng Chen Zhu verfasserin aut Image Processing for Picking Task of Random Ordered PET Drinking Bottles 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this research, six brands of soft drinks are decided to be picked up by a robot with a monocular Red Green Blue (RGB) camera. The drinking bottles need to be located and classified with brands before being picked up. The Mask Regional Convolutional Neural Network (R-CNN), a mask generation network improved from Faster R-CNN, is trained with common object in contest datasets to detect and generate the mask on the bottles in the image. The Inception v3 is selected for the brand classification task. Around 200 images are taken or found at first; then, the images are augmented to 1500 images per brands by using random cropping and perspective transform. The result shows that the masked image can be labeled with its brand name with at least 85% accuracy in the experiment. Image processing robotics picking deep learning COCO dataset Technology T Takafumi Matsumaru verfasserin aut In Journal of Robotics, Networking and Artificial Life (JRNAL) Atlantis Press, 2017 6(2019), 1 (DE-627)1005552487 23526386 nnns volume:6 year:2019 number:1 https://doi.org/10.2991/jrnal.k.190531.008 kostenfrei https://doaj.org/article/0ea523675d9f4713a78022839d0e36bf kostenfrei https://www.atlantis-press.com/article/125909657/view kostenfrei https://doaj.org/toc/2352-6386 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 1
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author	Chen Zhu
spellingShingle	Chen Zhu misc Image processing misc robotics picking misc deep learning misc COCO dataset misc Technology misc T Image Processing for Picking Task of Random Ordered PET Drinking Bottles
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topic_title	Image Processing for Picking Task of Random Ordered PET Drinking Bottles Image processing robotics picking deep learning COCO dataset
topic	misc Image processing misc robotics picking misc deep learning misc COCO dataset misc Technology misc T
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title	Image Processing for Picking Task of Random Ordered PET Drinking Bottles
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title_full	Image Processing for Picking Task of Random Ordered PET Drinking Bottles
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author_browse	Chen Zhu Takafumi Matsumaru
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author2-role	verfasserin
title_sort	image processing for picking task of random ordered pet drinking bottles
title_auth	Image Processing for Picking Task of Random Ordered PET Drinking Bottles
abstract	In this research, six brands of soft drinks are decided to be picked up by a robot with a monocular Red Green Blue (RGB) camera. The drinking bottles need to be located and classified with brands before being picked up. The Mask Regional Convolutional Neural Network (R-CNN), a mask generation network improved from Faster R-CNN, is trained with common object in contest datasets to detect and generate the mask on the bottles in the image. The Inception v3 is selected for the brand classification task. Around 200 images are taken or found at first; then, the images are augmented to 1500 images per brands by using random cropping and perspective transform. The result shows that the masked image can be labeled with its brand name with at least 85% accuracy in the experiment.
abstractGer	In this research, six brands of soft drinks are decided to be picked up by a robot with a monocular Red Green Blue (RGB) camera. The drinking bottles need to be located and classified with brands before being picked up. The Mask Regional Convolutional Neural Network (R-CNN), a mask generation network improved from Faster R-CNN, is trained with common object in contest datasets to detect and generate the mask on the bottles in the image. The Inception v3 is selected for the brand classification task. Around 200 images are taken or found at first; then, the images are augmented to 1500 images per brands by using random cropping and perspective transform. The result shows that the masked image can be labeled with its brand name with at least 85% accuracy in the experiment.
abstract_unstemmed	In this research, six brands of soft drinks are decided to be picked up by a robot with a monocular Red Green Blue (RGB) camera. The drinking bottles need to be located and classified with brands before being picked up. The Mask Regional Convolutional Neural Network (R-CNN), a mask generation network improved from Faster R-CNN, is trained with common object in contest datasets to detect and generate the mask on the bottles in the image. The Inception v3 is selected for the brand classification task. Around 200 images are taken or found at first; then, the images are augmented to 1500 images per brands by using random cropping and perspective transform. The result shows that the masked image can be labeled with its brand name with at least 85% accuracy in the experiment.
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title_short	Image Processing for Picking Task of Random Ordered PET Drinking Bottles
url	https://doi.org/10.2991/jrnal.k.190531.008 https://doaj.org/article/0ea523675d9f4713a78022839d0e36bf https://www.atlantis-press.com/article/125909657/view https://doaj.org/toc/2352-6386
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up_date	2024-07-03T13:34:14.230Z
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