A Hard Voting Policy-Driven Deep Learning Architectural Ensemble Strategy for Industrial Products Defect Recognition and Classification

A Hard Voting Policy-Driven Deep Learning Architectural Ensemble Strategy for Industrial Products Defect Recognition and Classification

Manual or traditional industrial product inspection and defect-recognition models have some limitations, including process complexity, time-consuming, error-prone, and expensiveness. These issues negatively impact the quality control processes. Therefore, an efficient, rapid, and intelligent model i...

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Bibliographic Details
Main Authors: Okeke Stephen, Samaneh Madanian, Minh Nguyen
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Sensors
Subjects:
deep learning ensemble
visual inspection
defect recognition and classification
voting policy
convolutional neural networks
industrial products
Online Access:https://www.mdpi.com/1424-8220/22/20/7846

Internet

https://www.mdpi.com/1424-8220/22/20/7846

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