Deep Learning-Based Recognition and Classification of Jin Cang Embroidery Stitches

Jin Cang embroidery, characterized by elaborate metallic threadwork and intricate textural patterns, is an important form of intangible cultural heritage. The digital preservation of Jin Cang embroidery is hindered by the scarcity of specialized datasets and the lack of object detection models that balance high performance with computational efficiency for edge deployment. To address these challenges, a dedicated dataset comprising 3050 images across eight core stitch categories is introduced as the first dataset of its kind for Jin Cang embroidery. Building upon this foundation, Lite-YOLOv11s, a domain-specific lightweight detection framework, is proposed with MobileNetV4 as its backbone to improve the extraction of high-frequency texture cues associated with metallic threadwork. Experimental results show that Lite-YOLOv11s achieves an [email protected] of 0.951, outperforming the YOLOv11s baseline (0.927) while reducing model parameters by 40% and FLOPs by 46%. EigenCAM visualizations further show that the model can localize discriminative stitch-level features even under complex backgrounds. This work provides an efficient and deployable solution for intelligent embroidery recognition and offers a useful reference for the digital preservation of other fine-grained cultural heritage crafts.