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A platform for research: civil engineering, architecture and urbanism
Computer vision application for improved product traceability in the granite manufacturing industry
The traceability of granite blocks consists in identifying each block with a finite number of colour bands that represent a numerical code. This code has to be read several times throughout the manufacturing process, but its accuracy is subject to human errors, leading to cause faults in the traceability system. A computer vision system is presented to address this problem through colour detection and the decryption of the associated code. The system developed makes use of colour space transformations and various thresholds for the isolation of the colours. Computer vision methods are implemented, along with contour detection procedures for colour identification. Lastly, the analysis of geometrical features is used to decrypt the colour code captured. The proposed algorithm is trained on a set of 109 pictures taken in different environmental conditions and validated on a set of 21 images. The outcome shows promising results with an accuracy rate of 75.00% in the validation process. Therefore, the application presented can help employees reduce the number of mistakes in product tracking.
Computer vision application for improved product traceability in the granite manufacturing industry
The traceability of granite blocks consists in identifying each block with a finite number of colour bands that represent a numerical code. This code has to be read several times throughout the manufacturing process, but its accuracy is subject to human errors, leading to cause faults in the traceability system. A computer vision system is presented to address this problem through colour detection and the decryption of the associated code. The system developed makes use of colour space transformations and various thresholds for the isolation of the colours. Computer vision methods are implemented, along with contour detection procedures for colour identification. Lastly, the analysis of geometrical features is used to decrypt the colour code captured. The proposed algorithm is trained on a set of 109 pictures taken in different environmental conditions and validated on a set of 21 images. The outcome shows promising results with an accuracy rate of 75.00% in the validation process. Therefore, the application presented can help employees reduce the number of mistakes in product tracking.
Computer vision application for improved product traceability in the granite manufacturing industry
J. Martínez (author) / X. Rigueira (author) / M. Araújo (author) / E. Giráldez (author) / A. Recamán (author)
2023
Article (Journal)
Electronic Resource
Unknown
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Automated Product Traceability
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