Recognizing materials in cultural relic images using computer vision and attention mechanism

Different cultural backgrounds give birth to distinct styles of cultural heritage. In order to explore the relationship between the cultural relics' materials and the specific cultural attributes, a new method for the recognition of cultural relics' image materials based on computer vision and attention mechanism is proposed. Moreover, this paper analyzes the relationship between Chinese traditional cultural relics and Chinese dynasties as an example. The methodology of this work consists first of using Python to collect and manually screen cultural relic images of common material types. Then, the nine datasets with different materials are uploaded to the EfficientNet-B0 network with the attention mechanism for iterative training. The best weight model is stored and put to the test. Finally, the improved EfficientNet-B0 network is applied to recognize the cultural relics image datasets of each dynasty, and the relationship between the materials and the cultural attributes of each dynasty is analyzed. As for the outcomes, the experimental results show that the EfficientNet-B0 model, with the attention mechanism, can effectively enhance the extraction of image material information, and the accuracy of the recognized cultural relics image materials reaches up to 88.15%, with an average precision of 83.3%. The comparison experiment on the material dataset shows that the proposed method has excellent ability in the recognition of material image compared with other common image recognition methods.