Abstract

The proposed study analyses a hybrid deep learning method to monitor a vehicle with drones with augmented simulated GPS data to increase awareness and localization accuracy. The system combines both the high detection speed of a real-time YOLOv5 with the high recognition accuracy of task-driven Faster R-CNN, which makes the performance of the system quite balanced, fully applicable to the application of aerial surveillance enforcement. The results will mimic realistic monitoring conditions since synthetic aerial scenes were produced in which vehicle density is randomly distributed and simulated geolocation data. Both models were applied in the processing of each scene and the resultant images were combined by a voting scheme. The hybrid system had an accuracy of 1.00, recalls 0.90, and F1 score of 0.95- it performed higher than the Faster R-CNN alone (F1 score:0.89) and higher in different conditions. The novelty of the proposed research is based on the fact that the invention combines the methods of dual-modality object detection (visual + spatial) and the use of a GPS base, which allows not only visual object detection but also object positioning. As opposed to the approaches previously used, based on single-modality models and without consideration of the data on geolocation, the framework achieves the integration of object recognition and useful mapping. The suggested system is lighttrack, economically feasible, and it is conveniently deployable to present scalable real-time traffic tracking, smart city planning, and aerial autonomy surveillance.