stdClass Object
(
    [id] => 7541
    [paper_index] => 202209-01-011199
    [title] => DRONE DETECTION USING DEEP LEARNING
    [description] => 
    [author] => V.K.G. Kalaiselvi, K. Poorna Pushkala, Hariharan Shanmugasundaram, Reenu Sivadarshini M, Deekshitha K, Dharshini S
    [googlescholar] => https://scholar.google.co.in/scholar?q=eprajournals.com&hl=en&scisbd=2&as_sdt=0,5
    [doi] => https://doi.org/10.36713/epra11199
    [year] => 2022
    [month] => September
    [volume] => 8
    [issue] => 9
    [file] => 30.EPRA JOURNALS 11199.pdf
    [abstract] => Drones have widespread application in real life and the industry is expanding rapidly. As they are growing increasingly it is more accessible to the public at cheaper prices.  They are used for espionage and can be converted into gruesome weapons. Hence it is very important to monitor and detect unauthorized drones entering into the restricted regions in order to maintain peace and prevent chaos. The Technology stack implied here is You Only Look Once (YOLO v5)  which is a real time object detection system. In recent times Yolo is a profound algorithm used for real time object or image detection . The Yolo trained model is trained with pictures of drones and birds so that the trained model can differentiate between and prevent false prediction of drones . Everytime a drone is detected in the camera an alert message is sent to the higher officials so that the drone could be eliminated. This algorithm comprises 3 techniques namely: Residual blocks, Bounding box regression and Intersection over Union. The camera used here is 360 degree so that maximum area of visibility is obtained.  
    [keywords] => Drones, real time object detection , YOLO v4.
    [doj] => 2022-09-12
    [hit] => 2161
    [status] => y
    [award_status] => P
    [orderr] => 30
    [journal_id] => 1
    [googlesearch_link] => https://www.google.com/search?q=DRONE+DETECTION+USING+DEEP+LEARNING+V.K.G.+Kalaiselvi%2C+K.+Poorna+Pushkala%2C+Hariharan+Shanmugasundaram%2C+Reenu+Sivadarshini+M%2C+Deekshitha+K%2C+Dharshini+S&rlz=1C1CHBD_enIN959IN959&sourceid=chrome&ie=UTF-8
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Multidisciplinary Research (IJMR)
    [short_code] => IJMR
    [eissn] =>  2455-3662 (Online)
    [pissn] => - --
    [home_page_wrapper] => images/products_image/11.IJMR.png
)
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