Waste reduction via image classification algorithms: beyond the human eye with an AI-based vision

Modern manufacturing is the world's largest and most automated industrial sector. The rise of Industry 4.0 technologies such as Big Data, Internet of Things (IoT) devices, and Machine Learning has enabled a better connection with machines and factory systems. Data harvesting allowed for a more...

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Published in:	International journal of production research Vol. 62; no. 9; pp. 3193 - 3211
Main Authors:	Shahin, Mohammad, Chen, F. Frank, Hosseinzadeh, Ali, Bouzary, Hamed, Shahin, Awni
Format:	Journal Article
Language:	English
Published:	London Taylor & Francis 02.05.2024 Taylor & Francis LLC
Subjects:	Algorithms Artificial intelligence Automation Big Data Customer satisfaction Damage detection Decision making Engineering Image classification Industrial applications Industry 4.0 Internet of Things Lean manufacturing Machine learning New technology Operations Research & Management Science Packages waste reduction
ISSN:	0020-7543, 1366-588X
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Abstract	Modern manufacturing is the world's largest and most automated industrial sector. The rise of Industry 4.0 technologies such as Big Data, Internet of Things (IoT) devices, and Machine Learning has enabled a better connection with machines and factory systems. Data harvesting allowed for a more seamless and comprehensive implementation of the knowledge-based decision-making process. New models that provide a competitive edge must be created by combining the Lean paradigm with the new technologies of Industry 4.0. This paper presents novel computer-based vision models for automated detection and classification of damaged packages from intact packages. In high-volume production environments, the package manual inspection process through the human eye consumes inordinate amounts of time poring over physical packages. Our proposed three different computer-based vision approaches detect damaged packages to prevent them from moving to shipping operations that would otherwise incur waste in the form of wasted operating hours, wasted resources and lost customer satisfaction. The proposed approaches were carried out on a data set consisting of package images and achieved high precision, accuracy, and recall values during the training and validation stage, with the resultant trained YOLO v7 model.
AbstractList	Modern manufacturing is the world's largest and most automated industrial sector. The rise of Industry 4.0 technologies such as Big Data, Internet of Things (IoT) devices, and Machine Learning has enabled a better connection with machines and factory systems. Data harvesting allowed for a more seamless and comprehensive implementation of the knowledge-based decision-making process. New models that provide a competitive edge must be created by combining the Lean paradigm with the new technologies of Industry 4.0. This paper presents novel computer-based vision models for automated detection and classification of damaged packages from intact packages. In high-volume production environments, the package manual inspection process through the human eye consumes inordinate amounts of time poring over physical packages. Our proposed three different computer-based vision approaches detect damaged packages to prevent them from moving to shipping operations that would otherwise incur waste in the form of wasted operating hours, wasted resources and lost customer satisfaction. The proposed approaches were carried out on a data set consisting of package images and achieved high precision, accuracy, and recall values during the training and validation stage, with the resultant trained YOLO v7 model. Not provided.
Author	Bouzary, Hamed Shahin, Mohammad Hosseinzadeh, Ali Shahin, Awni Chen, F. Frank
Author_xml	– sequence: 1 givenname: Mohammad surname: Shahin fullname: Shahin, Mohammad organization: The University of Texas at San Antonio – sequence: 2 givenname: F. Frank surname: Chen fullname: Chen, F. Frank email: FF.Chen@utsa.edu organization: The University of Texas at San Antonio – sequence: 3 givenname: Ali surname: Hosseinzadeh fullname: Hosseinzadeh, Ali organization: The University of Texas at San Antonio – sequence: 4 givenname: Hamed surname: Bouzary fullname: Bouzary, Hamed organization: The University of Texas at San Antonio – sequence: 5 givenname: Awni surname: Shahin fullname: Shahin, Awni organization: Mu'tah University
BackLink	https://www.osti.gov/biblio/2578642$$D View this record in Osti.gov
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SubjectTerms	Algorithms Artificial intelligence Automation Big Data Customer satisfaction Damage detection Decision making Engineering Image classification Industrial applications Industry 4.0 Internet of Things Lean manufacturing Machine learning New technology Operations Research & Management Science Packages waste reduction
Title	Waste reduction via image classification algorithms: beyond the human eye with an AI-based vision
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