Drone Movement Detection Network using Raspberry Pi


Authors : T.S.R. Krishna Prasad; P. Mahesh Kiran; M. Hima Sameera; P. Sri Sai Nachiketha; M. Mukesh Vamsi.

Volume/Issue : Volume 8 - 2023, Issue 3 - March

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://bit.ly/3GfDvCc

DOI : https://doi.org/10.5281/zenodo.7797284

Abstract : - This research paper proposes a system for detecting drones that use Raspberry Pi as its primary computing platform and implements the SSD MobileNetv2 architecture. The proposed approach involves training a machine learning model using deep learning and convolutional neural network algorithms. The SSD Mobilenetv2 architecture is proposed due to its accuracy and optimal performance in real-time object detection. The dataset includes images of numerous drones in various positions. The dataset has undergone image augmentations such as flipping, blurring, granulation and grayscale conversion, at random, before training. Multiple cameras, connected over a network, are connected to a Raspberry Pi employing star network topology with Raspberry Pi as the central hub. A dedicated machine, with the machine learning model running on it, accesses the video feeds from raspberry pi and infers them in real-time. The detection results are sent to the raspberry pi. Computer vision techniques are applied to the region of interest in the video feeds to determine the drone's trajectory. The system includes physical and digital alerts comprising alarm systems and SMS alerts so that authorities can be informed immediately whenever a drone is detected.

Keywords : Drones, Raspberry Pi, SSD MobileNetv2, realtime detection, star network topology, trajectory,SMS alerts

- This research paper proposes a system for detecting drones that use Raspberry Pi as its primary computing platform and implements the SSD MobileNetv2 architecture. The proposed approach involves training a machine learning model using deep learning and convolutional neural network algorithms. The SSD Mobilenetv2 architecture is proposed due to its accuracy and optimal performance in real-time object detection. The dataset includes images of numerous drones in various positions. The dataset has undergone image augmentations such as flipping, blurring, granulation and grayscale conversion, at random, before training. Multiple cameras, connected over a network, are connected to a Raspberry Pi employing star network topology with Raspberry Pi as the central hub. A dedicated machine, with the machine learning model running on it, accesses the video feeds from raspberry pi and infers them in real-time. The detection results are sent to the raspberry pi. Computer vision techniques are applied to the region of interest in the video feeds to determine the drone's trajectory. The system includes physical and digital alerts comprising alarm systems and SMS alerts so that authorities can be informed immediately whenever a drone is detected.

Keywords : Drones, Raspberry Pi, SSD MobileNetv2, realtime detection, star network topology, trajectory,SMS alerts

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