2022
DOI: 10.36227/techrxiv.21624585.v1
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SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network

Abstract: <p>Unmanned air vehicles (UAVs) popularity is on the rise as it enables the services like traffic monitoring, emergency communications, deliveries, and surveillance. However, the unauthorized usage of UAVs (a.k.a drone) may violate security and privacy protocols for security-sensitive national and international institutions. The presented challenges require fast, efficient, and precise detection of UAVs irrespective of harsh weather conditions, the presence of different objects, and their size to enable … Show more

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Cited by 3 publications
(6 citation statements)
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“…Object detection with computer vision improved the categorization and localization of targets. Such as YOLOv5, Faster-RCNN [4], and CenterNet [15]. The authors of [16] created an artifactual data set of drones and birds by removing the target's background and merging them with other images.…”
Section: Literature Reviewmentioning
confidence: 99%
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“…Object detection with computer vision improved the categorization and localization of targets. Such as YOLOv5, Faster-RCNN [4], and CenterNet [15]. The authors of [16] created an artifactual data set of drones and birds by removing the target's background and merging them with other images.…”
Section: Literature Reviewmentioning
confidence: 99%
“…To effectively process the datasets, the CNN include a significant number of parameters, and to minimize them, CNN uses filters [1] [2]. Object detection [3] necessitates many feature maps, each of which has hundreds of channels, making the model bloated and enormous [4]. Therefore, model compression is necessary for rapid deployment on embedding devices with fewer parameters [23].…”
Section: Ghost Auto Anchor Network (Gaanet)mentioning
confidence: 99%
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