Binary Classification Architecture for Edge Computing Based on Cognitive Services and Deep Neural Networks
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URI: http://hdl.handle.net/10835/16160
DOI: https://doi.org/10.1145/3508397.3564828
DOI: https://doi.org/10.1145/3508397.3564828
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Chancusig, Christian; Tumbaco, Sergio; Alulema, Darwin; Iribarne Martínez, Luis Fernando; Criado Rodríguez, JavierFecha
2022Resumen
Systems based on computer vision and artificial intelligence are an alternative for repetitive inspection processes. However, it is possible to extend the learning capacity of these systems to classify multiple objects using edge computing. This allows combining local processing with cloud processing to expand the possibilities and reduce the response time. In this work, a classification architecture based on remote web services and local neural networks is proposed. To test this architecture, Microsoft Azure cognitive web services and its Computer Vision API have been used, combined with the use of transfer learning and ResNet 50. The cloud service allows the identification and labelling of image content, while the Edge service, based on the neural network, allows the generation of classification models for those objects not identified or incorrectly identified by the remote service. The architecture allows to extend the possibility of image recognition by integrating web services tha...
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Microservices
Cyber_Physical Systems
Edge Computing
Computer Vision
Neural Network