Design and Development of an Autonomous Drone System for Real-Time Surveillance, Detection, and Data Fusion
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Abstract
As automation and real-time response are becoming crucial in emergency situations, industrial monitoring and safety in public spaces, the given study introduces the concept and engineering of the efficient and self-sufficient drone system capable of performing surveillance, threat identification, and prompt response activities at a relatively low cost. The capabilities encompass within the drone core a Raspberry Pi 5 as the processing unit including YOLO- based object and fire detection model, thermal and RGB cameras and sensors, GPS, LIDAR as well as an extinguisher for intervention in dangerous situations. This paper describes a reliable integrated data fusion technique that integrates various data streams for increased perception and identification of targets in complex scenarios. This doesn’t require using cloud-based services and relies on the improved YOLO model for object classification and fire detection on an edge computing platform. It self-organizes the movement using GPS and LIDAR map and crosses the field when comes on fire suppressing the fire with its frontal weapons. There are five important tests: the object recognition and detection accuracy test, the fire response test, the duration of operation of the sensor fusion system, the testing of autonomous navigation and mobility of the drone, and the communication capability test. It was noted earlier that the achieved results of a prototype successfully passed the trials with an average of the object detection accuracy of 91% and managed to suppress the fire in real time. The development cost was kept below ₹ 65000, with a target of achieving a unit manufacturing cost of ₹ 50000, which would help in making the system practical in real life. This work provides an innovative real-time UAV, sensor fusion, AI and Actuation, which this work lays down for future extension for the advancements in autonomous surveillance and safety systems.
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