UAV-Based Smart Parking Occupancy Detection and Management Using YOLOv11x-OBB

With the rapid growth of motor vehicle ownership across cities, parking lots attached to large commercial supermarkets and transportation hubs generally face the common pain points of delayed vacant parking spot scheduling and low manual inspection efficiency. The market urgently needs an intelligent parking management system that can quickly cover an entire site and report real-time updates of parking space status. To address this gap, this paper presents an integrated system for visual parking occupancy detection based on drone aerial imagery, combining an oriented-bounding-box vehicle detector, automatic parking-slot discovery and tracking, and a dual-role web platform for managers and customers. Rather than proposing a new detection algorithm, the contribution of this work lies in the automatic, annotation-free slot inference, tracking and gap-filling mechanism and in the end-to-end system integration that removes the need for manual slot mapping or per-site calibration. The system was field tested on three real-world parking lots of increasing size and density and achieved an occupancy-classification accuracy of 82.4% and F1 of 0.805 (detection F1 = 0.827 and [email protected] = 0.839) against manually annotated ground truth on 51 annotated frames, with a slot-discovery coverage (Slot Recall) of 0.833; these independently evaluated figures are prioritised over, as well as considerably lower than, the system’s own temporal-consistency detection scores of 98.7% accuracy/98.8% F1, which measures prediction stability without manual annotation. An itemised cost breakdown for a representative 10,000 m2 (~112-bay) lot indicates a deployment cost of RM6409 for the camera/UAV-based approach versus RM29,973 for an equivalent geomagnetic-sensor solution, which is approximately 21.4% of the geomagnetic up-front total (purchase and installation only); recurring UAV operating costs for continuous monitoring are discussed separately. The proposed approach offers a low-cost, sensor-free path toward intelligent parking management for large-scale lots.