Image-based recognition of intricate decorative motifs in complex visual environments remains a challenging task in computer vision due to geometric deformation, scale variation, illumination changes, and background interference. These challenges are particularly evident in images of animal motifs on Ming Dynasty Blue and White Porcelain, where curved vessel surfaces and decorative complexity significantly increase recognition difficulty. To facilitate robust model training and evaluation, this study establishes a dedicated annotated image dataset of animal motifs and expands it through targeted data augmentation strategies. Furthermore, an improved YOLOv8n-based object detection framework is proposed, featuring three key optimizations: (1) the CBS modules in the backbone and neck networks are replaced with Deformable Convolution Networks v2 (DCNv2) to strengthen the model’s feature extraction capability for deformed motifs; (2) an Efficient Multi-Scale Attention (EMA) mechanism is introduced into the neck network to integrate multi-scale features and suppress interference from complex decorative backgrounds; and (3) the original CIoU loss function is replaced with the MPDIoU loss function to improve localization accuracy and convergence speed. Experimental results demonstrate that the improved model achieves mAP@0.5 and mAP@0.5:0.95 values of 96.4% and 80.8%, respectively, representing improvements of 1.7% and 3.2% over the baseline model, while maintaining a detection speed of 89.3 FPS. These results indicate that the proposed framework provides an accurate, non-destructive, and efficient image-based method for cultural heritage object recognition. It can support museum collection management, archaeological analysis, and image-based visual sensing applications, while also showing potential for further optimization and deployment on compact edge-computing platforms.

Yuxian paper cutting is a distinctive regional form within UNESCO-recognized Chinese paper cutting. Its opera figure images remain difficult to organize at scale because interpretation and cataloguing rely heavily on manual expertise, while automated tools for preliminary annotation and retrieval remain limited. This study formulates a domain-specific object detection task and constructs a curated dataset of 926 original images containing 976 annotated instances across three operational visual categories: armor-clad, robe-wearing, and female-role figures. A YOLOv8n-based detector is adapted by integrating the Spatial and Channel Synergistic Attention (SCSA) module and the Inner-IoU regression strategy. On the test set, the resulting configuration achieves 95.8% precision, 92.9% recall, 98.8% mAP@0.5, and 89.6% mAP@0.5:0.95, exceeding the YOLOv8n baseline by 3.0, 3.6, 2.9, and 2.9 percentage points, respectively. It also achieves a forward-pass throughput of 92.3 FPS under the reported GPU configuration. Among the evaluated configurations, the combined SCSA placement with Inner-IoU achieved the highest overall detection performance under the current dataset and experimental protocol. The results support its use for preliminary visual-category annotation, retrieval, and organization of Yuxian paper-cut opera figure resources. However, the findings are based on a small domain-specific dataset and require further validation across alternative data partitions and broader heritage collections.

Han pictorial stones constitute an important component of China’s ancient cultural heritage, and their depictions of chariots, horses, and human figures provide valuable visual evidence for investigating travel activities in the Han dynasty. The identification and localization of chariot-and-horse elements provide a foundation for the retrieval and statistical analysis of relevant materials. However, current documentation practices rely largely on manual identification and transcription, limiting the efficiency of large-scale processing. In Han pictorial stone images, weak contours, textural interference, dense occlusion, and scale variation pose substantial challenges for reliable object detection. Accordingly, 539 images were collected and annotated with 3736 instances. Building on this dataset, an improved YOLO11n model was proposed, incorporating the RubbingDown multi-branch downsampling structure, C3k2GRM module, and task-adapted ASF multi-scale fusion pathway for information preservation, contextual representation, and cross-scale feature fusion. The proposed model achieved mAP@0.5, mAP@0.5:0.95, and recall of 88.0%, 54.5%, and 86.5%, respectively, representing improvements of 5.5, 2.8, and 9.9 percentage points, respectively, over the YOLO11n baseline. The model maintained 2.58 M parameters, while GFLOPs increased from 6.3 to 7.3. The detection outputs could support future workflows for documenting, retrieving, quantitatively analyzing, and manually verifying chariot-and-horse elements in Han pictorial stone images.