<![CDATA[Automatic road crack detection plays an essential role in infrastructure maintenance, where rapid and accurate visual inspection is required under real-world conditions. Although deep learning–based detection models have demonstrated promising performance, many existing approaches rely on computationally intensive architectures or require manual hyperparameter tuning, which limits their efficiency and real-time applicability. Moreover, the integration of lightweight detection models with automated hyperparameter optimization remains relatively underexplored.This study proposes an efficient road crack detection framework based on a lightweight YOLO11s architecture enhanced through automated hyperparameter optimization using Optuna on the DeepCrack dataset. The proposed methodology includes image preprocessing through data augmentation, normalization, and resizing to improve model robustness. Subsequently, key hyperparameters including learning rate, weight decay, dropout rate, and optimizer selection are automatically optimized to obtain the best model configuration. Experimental results indicate that the optimized YOLO11s model achieves a precision of 90.4%, recall of 86.8%, mAP@0.5 of 89.8%, and mAP@0.5:0.95 of 63.6% after 25 optimization trials. These results demonstrate that automated hyperparameter optimization can significantly improve detection performance while maintaining computational efficiency. The main contribution of this study lies in the systematic integration of automated hyperparameter tuning within a lightweight YOLO-based framework, providing a resource efficient and accurate solution suitable for real-time and large-scale road damage monitoring]]>
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