Distal radius fractures (DRFs) are among the most frequently encountered fractures in clinical practice, requiring accurate and timely diagnosis for effective treatment. Thus far, deep learning-based object detection models have shown promise in automating fracture detection, however the potential of newer architectures, such as YOLOv8 and YOLOv11, remains largely unexplored in medical imaging. This study systematically evaluates YOLOv8m, YOLOv8l, YOLOv11m, and YOLOv11l, comparing their performance against the commonly utilized Faster R-CNN model in the literature for DRF detection. A diverse dataset of wrist radiographs, including publicly available and real-world clinical images, is used for training and evaluation. The models are assessed based on precision, recall, F1-score, mean average precision (mAP), and inference speed. The results indicate that YOLOv11l outperforms all other models, achieving the highest precision of 96.5%, recall of 95.7%, and F1-score of 96.1% on the validation set, while Faster R-CNN demonstrated the worst performance. The findings set a benchmark for selecting the most effective deep learning model for automated fracture detection, which enables the integration of AI-assisted diagnosis into clinical workflows and medical education.