JoWUA

Abstract

Since unmanned aerial vehicles (UAVs) provide real-time monitoring of vast areas, their rapid development has been crucial to the advancement of surveillance applications. However, in the face of complex environments, present surveillance systems frequently suffer from an initial lack of efficiency, scalability, and adaptability. In order to detect and track any security threats in real time, this study aims to create a unique AI-based aerial surveillance framework that makes use of CNNs and Fast R-CNNs. It trains and validates object identification models using publicly accessible UAV datasets in relation to important parameters like robustness, processing speed, and accuracy. The suggested framework for object detection using augmented intelligence thus applies to contemporary surveillance systems, which are designed to be reliable, resilient, and able to effectively satisfy contemporary security requirements. This study presents a brand-new, incredibly effective Faster R-CNN created especially to tackle the difficult object placement issue in aerial photos. For pinpointing the precise location of things of interest, the algorithm works incredibly well. The average accuracy has increased significantly to above 70%, according to the results. With an F1-score of 92.7%, the Fast R-CNN model achieved precision and recall scores of 93.1% and 92.4%, respectively, while still performing within the average of 94.7%.