An algorithm based on lightweight semantic features for ancient mural element object detection

The ancient mural paintings unearthed in China are precious world cultural heritages, which record the historical information of various eras and serve as valuable image materials for studying ancient Chinese society. The elements of the murals include figures, carriages, flowers, birds, and auspicious clouds. The digital research on these elements can better help us understand history and culture. In this paper, we have established a large-scale target detection dataset for mural elements excavated from ancient China, featuring a rich variety of labeled sample categories that span across different historical periods and regions, which provides significant value for the study of ancient Chinese history. Meanwhile, to address the defects present in the mural paintings, we have developed an adaptive random erasing augmentation algorithm, which forces the model to learn more comprehensive feature information, enabling it to adapt to the defective scenarios of the mural paintings. Moreover, we have created a target semantic feature extraction model for elements of ancient Chinese murals, which utilizes contextual information and residual attention mechanism to capture the semantic information, thereby enhancing the accuracy of element target detection. Finally, we have conducted a comparative analysis of the detection results of our proposed method with several other state-of-the-art target detection algorithms on the created mural dataset, and the visualization results validated the superiority of our proposed method.