Abstract
Computer vision combines cameras, edge or cloud computing, software and artificial intelligence (AI) to enable systems to automatically recognise and identify objects. In this context, this paper details machine intelligence and data analysis algorithms that can be used for real-time object recognition. This task is essential to design an automatic vision system that can be integrated in multiple applications in future sustainable smart cities, for example, self-driving cars, robots for home help/assistance or security. Therefore, this paper is firstly a bibliographical study of algorithms for real time object recognition, and secondly a comparison of these different approaches. The comparison will consist in highlighting the specificities of each algorithm and the common points or similarities that may exist between them. Also, the results of a concrete comparison in terms of time and recognition rate were also reported. The algorithms that were included in this study are: Convolutional Neural Network (CNN), Region-based CNN (R-CNN), Fast R-CNN, Faster R-CNN for region-based algorithms, and YOLO, Tiny-YOLO, Nano-YOLO, Mini-YOLO, Slim-YOLO, MobileNet, Single Shot Multibox Detector (SSD), and RetinaNet for non-region-based algorithms. Furthermore, this review addresses in its different sections the issue of implementing AI algorithms, whether in software or hardware architectures, or in a co-design approach which is considered an appropriate method capable of simultaneously taking advantage of software and hardware advantages.
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Wiem, B., Chabha, H., Ahmed, K. (2021). Computational Intelligence for Automatic Object Recognition for Vision Systems. In: Ghosh, U., Maleh, Y., Alazab, M., Pathan, AS.K. (eds) Machine Intelligence and Data Analytics for Sustainable Future Smart Cities. Studies in Computational Intelligence, vol 971. Springer, Cham. https://doi.org/10.1007/978-3-030-72065-0_15
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