Object detection methods that use deep learning or machine learning techniques are essential for recognizing and locating objects in pictures. To identify the best approach and model, this study explores the nuances of three well-known algorithms: Faster R-CNN, YOLOv7, and YOLOv8. We thoroughly assessed the performance using precision, recall, mean Average Precision (mAP), and confusion matrix metrics, leveraging a proprietary Tourism dataset. Our tests demonstrated a significant improvement in accuracy, with YOLOv8 setting the standard with a remarkable 93.4% accuracy, indicating its potential to completely transform object recognition in the tourism domain.