Lightweight Detection of Stone Inscriptions Based on an Improved YOLOv11n Model

To address prevalent challenges in stone inscription character detection—including glyph blurring, incompleteness, densely arranged characters, and substantial inter-object scale variation—this paper proposes PMN-YOLO, a lightweight, enhanced detection model built upon YOLOv11n. To enhance the detection performance of stele characters in complex scenarios, PP-LCNet is introduced to replace the original feature extraction structure. This not only reduces the model complexity but also enhances the feature expression ability for complex textures and blurred characters, providing effective support for subsequent recognition tasks. Consequently, the proposed model achieves a better balance between detection precision and computational efficiency. Second, we designed the MSAM-smallTarget module specifically for detecting small targets. By integrating multi-scale convolutional operations with spatial attention mechanisms, this module significantly enhances the model’s ability to perceive fine-grained features as well as blurred or fragmented characters commonly found in inscriptions. Furthermore, leveraging small convolutional kernels, dilated convolutions, and lightweight convolutional designs enables adaptive expansion of the receptive field while effectively constraining parameter count. Third, the NWD loss function based on the Wasserstein distance is introduced to replace the traditional IoU metric with a distribution-based similarity measure, thereby significantly improving the model’s localization robustness in scenarios involving densely distributed targets and ambiguous boundaries. The experimental results show that on the self-built stone inscription dataset, the precision of PMN-YOLO reaches 98.3%, the recall rate is 75.5%, and the mAP@50 and mAP@50-95 are 82.7% and 65.0% respectively. The model has 8.7% fewer parameters than the baseline. This method achieves lightweight performance and high detection accuracy, delivering a practical approach to automatically detect and digitally safeguard stone inscriptions.