This study investigates the automated detection of decorative motifs on Qing Dynasty banner shoes and systematically evaluates the effects of model architectures, annotation strategies, and optimization methods on detection performance, while examining the critical role of task definition in guiding technology selection. Two annotation schemes, namely "macro-level holistic" and "macro-patch joint", were designed, and comparative experiments were conducted on the YOLOv8 and YOLOv11 series under different task settings. In addition, a spectral-spatial attention module (SSAM) was introduced, and transfer learning based on pre-trained weights was employed to perform fine-grained category analysis and ablation experiments. The results show that SSAM yields significant improvements on large-scale models (YOLOv8x), although its effectiveness is highly sensitive to the capacity of the base model. The optimal model selection depends on the annotation strategy. Under the macro-level holistic task setting, YOLOv8x performs the best, and the YOLOv8 series generally outperforms the YOLOv11 series of comparable scale. At the category level, notable performance differences are observed, with textual motifs being detected relatively reliably, while animal motifs remain the primary challenge. Furthermore, the study finds that transfer learning is a key factor in achieving high performance, with its contribution significantly exceeding that of individual structural modifications. These findings establish a paradigm of "task-definition-driven technology selection", providing empirical evidences and methodological references for fine-grained object detection in cultural heritage digitization.
Key words
Qing Dynasty banner shoes /
annotation strategy /
YOLOv8 /
YOLOv11 /
spectral-spatial attention module (SSAM)
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