Enhancing Vision-Based Religious Tourism Systems in Makkah Using Fine-Tuned YOLOv11 for Landmark Detection

Makkah, one of the most significant cities in the Islamic world, possesses a rich architectural and cultural heritage that requires precise detection and identification of its landmarks. Accurate landmark detection plays a vital role in urban planning, cultural preservation, and enhancing tourism experiences. In this study, a fine-tuned versions of the YOLOv11 network, specifically the nano and small variants, are proposed for efficient and precise detection of Makkah’s landmarks. The YOLOv11 framework, renowned for its real-time object detection capabilities, was carefully adapted to address the unique challenges posed by the diverse visual characteristics of Makkah’s landmarks, including varying scales, intricate textures, and challenging environmental conditions. To further enhance the models for deployment in embedded systems with low-latency requirements, a quantization technique is applied. This process significantly reduces model size and increases inference speed, optimizing the network for resource-constrained environments while maintaining high detection accuracy. Beyond technical improvements, this approach supports real-world applications such as interactive tourism via mobile and AR systems, automated heritage documentation, and continuous monitoring of historic sites for conservation efforts. Additionally, integration into smart city infrastructures can enhance security and management of cultural landmarks. Experimental results show that the fine-tuned YOLOv11 models, particularly the small version, achieve high accuracy, with notable improvements in precision and recall compared to baseline models. This research demonstrates the potential of deep learning techniques for cultural heritage detection and lays the foundation for future applications in urban analytics, geospatial mapping, and real-time vision-based systems for tourism and heritage preservation.