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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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NA0004003
USDOE National Nuclear Security Administration (NNSA)
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2023.2225652