Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly

Molds are still assembled manually because of frequent demand changes and the requirement for comprehensive knowledge related to their high flexibility and adaptability in operation. We propose the application of human-robot collaboration (HRC) systems to improve manual mold assembly. In the existin...

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Autores principales:	Yee Yeng Liau, Kwangyeol Ryu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	human-robot collaboration transfer learning status recognition mold assembly Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/e652ce9034104bcb8f17d3b73c7550fc
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id	oai:doaj.org-article:e652ce9034104bcb8f17d3b73c7550fc
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spelling	oai:doaj.org-article:e652ce9034104bcb8f17d3b73c7550fc2021-11-11T19:42:02ZStatus Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly10.3390/su1321120442071-1050https://doaj.org/article/e652ce9034104bcb8f17d3b73c7550fc2021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/12044https://doaj.org/toc/2071-1050Molds are still assembled manually because of frequent demand changes and the requirement for comprehensive knowledge related to their high flexibility and adaptability in operation. We propose the application of human-robot collaboration (HRC) systems to improve manual mold assembly. In the existing HRC systems, humans control the execution of robot tasks, and this causes delays in the operation. Therefore, we propose a status recognition system to enable the early execution of robot tasks without human control during the HRC mold assembly operation. First, we decompose the mold assembly operation into task and sub-tasks, and define the actions representing the status of sub-tasks. Second, we develop status recognition based on parts, tools, and actions using a pre-trained YOLOv5 model, a one-stage object detection model. We compared four YOLOv5 models with and without a freezing backbone. The YOLOv5l model without a freezing backbone gave the optimal performance with a mean average precision (mAP) value of 84.8% and an inference time of 0.271 s. Given the success of the status recognition, we simulated the mold assembly operations in the HRC environment and reduced the assembly time by 7.84%. This study improves the sustainability of the mold assembly from the point of view of human safety, with reductions in human workload and assembly time.Yee Yeng LiauKwangyeol RyuMDPI AGarticlehuman-robot collaborationtransfer learningstatus recognitionmold assemblyEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12044, p 12044 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	human-robot collaboration transfer learning status recognition mold assembly Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	human-robot collaboration transfer learning status recognition mold assembly Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Yee Yeng Liau Kwangyeol Ryu Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly
description	Molds are still assembled manually because of frequent demand changes and the requirement for comprehensive knowledge related to their high flexibility and adaptability in operation. We propose the application of human-robot collaboration (HRC) systems to improve manual mold assembly. In the existing HRC systems, humans control the execution of robot tasks, and this causes delays in the operation. Therefore, we propose a status recognition system to enable the early execution of robot tasks without human control during the HRC mold assembly operation. First, we decompose the mold assembly operation into task and sub-tasks, and define the actions representing the status of sub-tasks. Second, we develop status recognition based on parts, tools, and actions using a pre-trained YOLOv5 model, a one-stage object detection model. We compared four YOLOv5 models with and without a freezing backbone. The YOLOv5l model without a freezing backbone gave the optimal performance with a mean average precision (mAP) value of 84.8% and an inference time of 0.271 s. Given the success of the status recognition, we simulated the mold assembly operations in the HRC environment and reduced the assembly time by 7.84%. This study improves the sustainability of the mold assembly from the point of view of human safety, with reductions in human workload and assembly time.
format	article
author	Yee Yeng Liau Kwangyeol Ryu
author_facet	Yee Yeng Liau Kwangyeol Ryu
author_sort	Yee Yeng Liau
title	Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly
title_short	Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly
title_full	Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly
title_fullStr	Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly
title_full_unstemmed	Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly
title_sort	status recognition using pre-trained yolov5 for sustainable human-robot collaboration (hrc) system in mold assembly
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e652ce9034104bcb8f17d3b73c7550fc
work_keys_str_mv	AT yeeyengliau statusrecognitionusingpretrainedyolov5forsustainablehumanrobotcollaborationhrcsysteminmoldassembly AT kwangyeolryu statusrecognitionusingpretrainedyolov5forsustainablehumanrobotcollaborationhrcsysteminmoldassembly
_version_	1718431498096345088

Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly

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