Image-Based Recognition of Intricate Animal Motifs on Ming Dynasty Blue and White Porcelain Using an Improved YOLOv8n Model.

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.