Abstract:
During the last decade, Unmanned Aerial Vehicles (UAVs) have found their way into
different industries ranging from mapping, 3D modeling to thermal inspection. They
offer some major benefits over the traditional methods including improvement in the
accuracy using high-end sensors, reduction in cost and autonomous operation without
any human intervention. Surveillance has also employed drones to monitor larger areas
of land from air however generally a human pilot operates or monitors the drone during
its flight. In this study, an end-to-end surveillance system is simulated in Gazebo that
can provide the object detection functionality using Artificial Intelligence (AI) and can
operate independent of any human being. A PID controller is designed and used for
precision landing on an ARUCO marker while You Only Look Once (YOLO) algorithm
is used to detect and localize objects during the assigned mission. Study indicates
encouraging results in detection accuracy and landing precision, suggesting potential
for extensive application in different surveillance scenarios. Future work will include
real-world deployment and integration with current security infrastructures.
