YOLOv10-TWD: An Improved YOLOv10n for Terracotta Warrior Recognition

To address challenges such as complex backgrounds, partial occlusion, and high similarity of details in Terracotta Warrior image recognition, this paper proposes a lightweight detection method, YOLOv10-TWD, based on an improved YOLOv10n. Specifically, a lightweight Convolution-Attention Fusion Module (CAFMAttention) and a dual-branch feature extraction structure (DualConv) are integrated into the detection head to enhance the model’s focus on fine-grained features and its discriminative robustness under partial damage conditions. In the Neck network, Ghost-Shuffle Convolution (GSConv) is introduced to compress the computational cost of multi-scale feature fusion while strengthening context-aware capabilities. Experimental results on a self-built Terracotta Warrior dataset demonstrate that the proposed method achieves a 7.63% improvement in [email protected] compared to the baseline YOLOv10n, while simultaneously achieving a 6.66% increase in inference speed. The model achieves high precision alongside significant optimization in inference efficiency, making it well-suited for rapid recognition tasks in cultural heritage and museum scenarios.