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Title	Visual Perception For Smart City Defense Administration And Intelligent Premonition Framework Based On Dnn
ID_Doc	61281
Authors	Neogi D.; Das N.; Deb S.
Year	2022
Published	Studies in Computational Intelligence, 1002
DOI	http://dx.doi.org/10.1007/978-981-16-7498-3_7
Abstract	A detailed methodology of object detection in a smart city setting has been illustrated in this chapter. The presented methodology focuses on intelligent uses of machine learning and deep learning algorithms for the effective extraction of the desired ROI from a challenging backdrop. This chapter encompasses a Convolutional Neural Network (CNN)-based architecture and a Faster RCNN-based approach and holistic comparisons are being made between the results obtained from the different approaches. The problem that has been tried to address through this work is the lack of robust algorithms that can detect occluded objects effectively, which may commonly occur in a smart city. So, the primary focus of this work is to devise a methodology that can detect even minute objects camouflaged in a city crowd, which are prevalent in smart cities across India. This work is believed to be beneficial in various sectors, even in the military, to help them with reconnaissance tasks. Further, the proposed methodology is equipped with an alarm system that warns against plausible security breaches and intrusion. This mechanism enhances the security of a smart city using IoT techniques. The entire methodology described in the chapter can be deployed without the use of any additional hardware. Overall, the proposed framework is robust, effective and viable for multi-facet uses in the future and can be effectively deployed in large distributed systems across smart cities of India. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	Alerting framework; CNN; Defense; Faster RCNN; Motional feedback; Object detection; ROI; Smart city; Smart city

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