Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This s...

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Bibliographic Details
Main Authors: Paulo Henrique Martinez Piratelo, Rodrigo Negri de Azeredo, Eduardo Massashi Yamao, Jose Francisco Bianchi Filho, Gabriel Maidl, Felipe Silveira Marques Lisboa, Laercio Pereira de Jesus, Renato de Arruda Penteado Neto, Leandro dos Santos Coelho, Gideon Villar Leandro
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Machines
Subjects:
convolutional neural networks
warehouse management
image classification
ensemble learning
synthetic data
depth image
Online Access:https://www.mdpi.com/2075-1702/10/1/28

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