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

Abstract

With 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.