Real-time Implementation of RMNv2 Classifier in NXP Bluebox 2.0 and NXP i.MX RT1060

dc.contributor.authorAyi, Maneesh
dc.contributor.authorEl-Sharkawy, Mohamed
dc.contributor.departmentElectrical and Computer Engineering, School of Engineering and Technologyen_US
dc.date.accessioned2022-02-18T21:45:26Z
dc.date.available2022-02-18T21:45:26Z
dc.date.issued2020-08
dc.description.abstractWith regards to Advanced Driver Assistance Systems in vehicles, vision and image-based ADAS is profoundly well known since it utilizes Computer vision algorithms, for example, object detection, street sign identification, vehicle control, impact cautioning, and so on., to aid sheltered and smart driving. Deploying these algorithms directly in resource-constrained devices like mobile and embedded devices etc. is not possible. Reduced Mobilenet V2 (RMNv2) is one of those models which is specifically designed for deploying easily in embedded and mobile devices. In this paper, we implemented a real-time RMNv2 image classifier in NXP Bluebox 2.0 and NXP i.MX RT1060. Because of its low model size of 4.3MB, it is very successful to implement this model in those devices. The model is trained and tested with the CIFAR10 dataset.en_US
dc.eprint.versionAuthor's manuscripten_US
dc.identifier.citationAyi, M., & El-Sharkawy, M. (2020). Real-time Implementation of RMNv2 Classifier in NXP Bluebox 2.0 and NXP i.MX RT1060. 2020 IEEE Midwest Industry Conference (MIC), 1, 1–4. https://doi.org/10.1109/MIC50194.2020.9209615en_US
dc.identifier.urihttps://hdl.handle.net/1805/27880
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/MIC50194.2020.9209615en_US
dc.relation.journal2020 IEEE Midwest Industry Conferenceen_US
dc.rightsPublisher Policyen_US
dc.sourceAuthoren_US
dc.subjectconvolutional neural networksen_US
dc.subjectdeep neural networken_US
dc.subjectCIFAR-10en_US
dc.titleReal-time Implementation of RMNv2 Classifier in NXP Bluebox 2.0 and NXP i.MX RT1060en_US
dc.typeConference proceedingsen_US
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