Deep Learning and Real TIME Computer Vision Based Feature Matching in Flying Adhoc Networks

Flying Adhoc Networks (FANETs) are getting huge popularity in assorted applications for civilian, corporate and defense applications. FANET is a specialized class of the Mobile Adhoc Networks (MANET) having computer vision, sensor devices and the Global Positioning based System (GPS) for live monitoring and logging the environment under surveillance. From the past few period, FANETs were widely and rapidly used for the monitoring and controlling of scenarios civil wars and local commotions. FANET makes used of assorted Unmanned Aerial Vehicle (UAV) which is useful and because of which the pre-programmed plans running on the flights of such a type of flying and other means of possible related objects were implemented.UAV describes pre-programmed and structured flying object which can also meats aircraft without the need of any kind of dedicated pilot on boarded. This research paper is focusing on the real time integration of UAV based real-time deep learning methods with real time extraction and feature matching techniques from the camera of relevant flying FANET aircrafts and because of which the definite and accurate target can be easily extracted. The proposed manuscript is presenting the effective way of technique in both civil as well as in military defense and thereby to fully recognize the enhanced activities of many underlying suspicious person which were targeted and also to locate the exact flying objects which were being released exactly by the opponent (or) targeted country.In the proposed manuscript, the real time extraction and integration of the OpenCV with additional feature descriptors and extractors in the absolute form of camera for the deep learning based real time FANET is being proposed and evident that the method and strategic tactical decisions were made by the explicit use of this proposed methodology in real time occurrences. This proposed empirical research proposal is targeting the effective integration of the high performance and super-computing-based library OpenCV because of which the high dimensional and high efficacy evaluation by the features that actually can be extracted have been analyzed. This feature-based point was found to be projected to be evaluated on the base of corners, edges and inner parameters of the real-time image in FANET