Object Detection for Cargo Unloading System Based on Fuzzy C Means
With the recent increase in the utilization of logistics and courier services, it is time for research on logistics systems fused with the fourth industry sector. Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence fie...
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| Vydáno v: | Computers, materials & continua Ročník 71; číslo 2; s. 4167 - 4181 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Henderson
Tech Science Press
2022
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| ISSN: | 1546-2226, 1546-2218, 1546-2226 |
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| Abstract | With the recent increase in the utilization of logistics and courier services, it is time for research on logistics systems fused with the fourth industry sector. Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field, but so far, algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further development. In this study, the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study, and a cargo object recognition algorithm applicable to the automatic loading device is proposed to improve the amorphous cargo identification performance. The fuzzy convergence algorithm is an algorithm that applies Fuzzy C Means to existing algorithm forms that fuse YOLO(You Only Look Once) and Mask R-CNN(Regions with Convolutional Neuron Networks). Experiments conducted using the fuzzy convergence algorithm showed an average of 33 FPS(Frames Per Second) and a recognition rate of 95%. In addition, there were significant improvements in the range of actual box recognition. The results of this study can contribute to improving the performance of identifying amorphous cargoes in automatic loading devices. |
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| AbstractList | With the recent increase in the utilization of logistics and courier services, it is time for research on logistics systems fused with the fourth industry sector. Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field, but so far, algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further development. In this study, the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study, and a cargo object recognition algorithm applicable to the automatic loading device is proposed to improve the amorphous cargo identification performance. The fuzzy convergence algorithm is an algorithm that applies Fuzzy C Means to existing algorithm forms that fuse YOLO(You Only Look Once) and Mask R-CNN(Regions with Convolutional Neuron Networks). Experiments conducted using the fuzzy convergence algorithm showed an average of 33 FPS(Frames Per Second) and a recognition rate of 95%. In addition, there were significant improvements in the range of actual box recognition. The results of this study can contribute to improving the performance of identifying amorphous cargoes in automatic loading devices. |
| Author | Park, Jaemin Won, Jongun Kim, Youngmin Hwang, Sunwoo Kwon, Yongjang |
| Author_xml | – sequence: 1 givenname: Sunwoo surname: Hwang fullname: Hwang, Sunwoo – sequence: 2 givenname: Jaemin surname: Park fullname: Park, Jaemin – sequence: 3 givenname: Jongun surname: Won fullname: Won, Jongun – sequence: 4 givenname: Yongjang surname: Kwon fullname: Kwon, Yongjang – sequence: 5 givenname: Youngmin surname: Kim fullname: Kim, Youngmin |
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| Cites_doi | 10.7232/JKIIE.2020.46.1.071 10.20462/TeBS.2020.04.21.2.17 10.32604/cmc.2021.016871 10.32604/cmc.2021.017480 10.1126/science.1127647 10.32604/cmc.2020.011191 10.32604/cmc.2021.018781 10.1016/j.neunet.2005.06.042 10.1109/TPAMI.2009.167 10.5302/J.ICROS.2015.15.0157 10.32604/cmc.2020.05317 10.5302/J.ICROS.2015.15.0027 10.5391/JKIIS.2019.29.6.430 10.32604/cmc.2021.015249 10.32604/cmc.2021.018461 |
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| Title | Object Detection for Cargo Unloading System Based on Fuzzy C Means |
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