An Ensemble Deep Learning Framework for Smart Tourism Landmark Recognition Using Pixel-Enhanced YOLO11 Models

Tourist destination classification is pivotal for enhancing the travel experience, supporting cultural heritage preservation, and enabling smart tourism services. With recent advancements in artificial intelligence, deep learning-based systems have significantly improved the accuracy and efficiency of landmark recognition. To address the limitations of existing datasets, we developed the Samarkand dataset, containing diverse images of historical landmarks captured under varying environmental conditions. Additionally, we created enhanced image variants by squaring pixel values greater than 225 to emphasize high-intensity architectural features, improving the model’s ability to recognize subtle visual patterns. Using these datasets, we trained two parallel YOLO11 models on original and enhanced images, respectively. Each model was independently trained and validated, preserving only the best-performing epoch for final inference. We then ensembled the models by averaging the model outputs from the best checkpoints to leverage their complementary strengths. Our proposed approach outperforms conventional single-model baselines, achieving an accuracy of 99.07%, precision of 99.15%, recall of 99.21%, and F1-score of 99.14%, particularly excelling in challenging scenarios involving poor lighting or occlusions. The model’s robustness and high performance underscore its practical value for smart tourism systems. Future work will explore broader geographic datasets and real-time deployment on mobile platforms.