In computer artificial intelligence, there is great potential in research on the protection of Suzhou's traditional gardens, a world cultural heritage site. As a special material in Suzhou's traditional garden architecture, shedthin tile is widely used in roof base laying and is one of the important materials for building roofs. However, professionals need to reach the roof and spend much time and effort assessing the damage before repairing it. Therefore, the main goals of this study are to investigate a machine learning-based method for finding targets and determining the type of surface damage on a shedthin tile using the YOLOv4 model trained in this study. Using 500 shedthin tile on-site photos as training samples, the model was trained for 750 epochs. The main results of this study are as follows: (1) An object detection method based on machine learning can efficiently and accurately identify damage content, overcoming the manpower and time–cost limitations of traditional assessment methods. (2) The detection model in this study has an accuracy of 85.89% for water stain recognition of shedthin tiles, 93.29% for surface scaling, 87.37% for color aberration, and 96.15% for too wide a gap. The comprehensive accuracy is 90.20%, which meets the basic testing requirements. (3) The model demonstrated its robustness and reliability in complex environments in application tests in actual scenarios, providing a methodological reference for computer vision and target detection technology in cultural heritage protection.

With the rapid development of machine learning technology, it has become possible to automatically identify cultural heritage elements in traditional buildings. This research aimed to develop a machine learning model based on the YOLOv4 architecture to identify the traditional Chinese porcelain inlay pattern in the Lingnan region. The researchers collected and annotated a large quantity of Lingnan Chinese porcelain inlay image data and then used these data to train the studied model. The research results show that (1) the model in this study was specifically adjusted to effectively identify a variety of Chinese porcelain inlay pattern types, including traditional patterns such as plum blossoms and camellias. (2) In the 116th epoch, the model showed excellent generalization ability, and the verification loss reached the lowest value of 0.88. The lowest training loss in the 195th epoch was 0.99, indicating that the model reached an optimal balance point for both recognition accuracy and processing speed. (3) By comparing different models for detecting Chinese porcelain inlay images across 581 pictures, our YOLOv4 model demonstrated greater accuracy in most classification tasks than did the YOLOv8 model, especially in the classification of chrysanthemums, where it achieved an accuracy rate of 87.5%, significantly outperforming YOLOv8 by 58.82%. However, the study also revealed that under certain conditions, such as detecting apples and pears in low-light environments, YOLOv8 showed a lower missing data rate, highlighting the limitations of our model in dealing with complex detection conditions.

With the rapid growth of global urbanization and the rising demand for sustainable development, it is essential to study the performance and durability of building materials. As a traditional and widely employed building material, Chinese clay tiles play a significant role in traditional Chinese architecture. However, traditional methods for assessing structural surface damage necessitate the time-consuming and labor-intensive assessment and judgment of trained professionals. Consequently, it is crucial to employ machine learning techniques for automatic damage type identification. This study identifies the types of damage to Chinese clay tiles in Macau by employing machine learning techniques and the YOLOv4 object detection model. A total of 363 photographs of on-site Chinese clay tiles were used as training samples, and 200 epochs of the model training were performed. The primary findings of this study are as follows: (1) The machine learning method, based on the YOLOv4 model, provides an effective and precise solution for the automatic identification of Chinese clay tiles damage types, overcoming the human and time-cost constraints of conventional evaluation methods. (2) The detection accuracy of the detection model in this study is 95.42% for the detection of Chinese clay tiles cracks, 80.91% for the detection of stains, and 89.34% for the detection of surface wear, with an overall accuracy of 88.98%, which meets the basic detection requirements. (3) The experimental results demonstrate the viability and efficacy of the proposed method for identifying clay tile damage types and provide a method reference for the preservation and sustainable development of historical buildings.

The Shanhaiguan Great Wall is a section of the Great Wall of the Ming Dynasty, which is a UNESCO World Heritage Site. Both sides of its basic structure are composed of rammed earth and gray bricks. The surface gray bricks sustain damage from environmental factors, resulting in a decline in their structural quality and even a threat to their safety. Traditional surface damage detection methods rely primarily on manual identification or manual identification following unmanned aerial vehicle (UAV) aerial photography, which is labor-intensive. This paper applies the YOLOv4 machine learning model to the gray surface bricks of the Plain Great Wall of Shanhaiguan as an illustration. By slicing and labeling the photos, creating a training set, and then training the model, the proposed approach automatically detects four types of damage (chalking, plants, ubiquinol, and cracking) on the surface of the Great Wall. This eliminates the need to expend costly human resources for manual identification following aerial photography, thereby accelerating the work. Through research, it is found that 1) compared with manual detection, this method can quickly and efficiently monitor a large number of wall samples in a short period of time and improve the efficiency of brick wall detection in ancient buildings. 2) Compared with previous approaches, the accuracy of the current method is improved. The identifiable types are increased to include chalking and ubiquinol, and the accuracy rate increases by 0.17% (from 85.70% before to 85.87% now). 3) This method can quickly identify the damaged parts of the wall without damaging the appearance of the historical building structure, enabling timely repair measures.

The floor plan layout of museum exhibition spaces is the skeleton network of the museum, which determines the internal circulation and spatial form of the museum. This paper studies the method and practice of using artificial intelligence technology to assist in the space design of exhibition halls in urban cultural museums. First, it introduces the limitations of traditional space design methods for exhibition halls in urban cultural museums and the superiority and application prospects of the CGAN (conditional generative adversarial network) model in space design. Second, the principle and training process of the CGAN model are explained in detail, and the experimental results and analysis are given. By learning 100 floor plans of exhibition halls of urban culture museums, the CGAN model can generate a new floor plan design for an exhibition hall, which provides a new idea and innovative method for this design task. Finally, the limitations and future research directions of the CGAN model in the space design of urban cultural museum exhibition halls are discussed. The study shows that using the CGAN model to learn the floor plans of exhibition halls of urban cultural museums can effectively improve the innovation and practicability of space design and has the following advantages: (1) It can quickly generate a large number of exhibition hall floor plans, shorten the design cycle, and improve design efficiency. (2) The generated floor plan designs of the exhibition hall are diverse and personalized, meeting the design requirements of different scenarios and needs. (3) The method promotes the deep integration of space design and artificial intelligence technology and provides new possibilities and ideas for space design. These conclusions provide new ideas and methods for the space design of exhibition halls of urban cultural museums and provide a reference and inspiration for space design and intelligent applications in other fields, such as office space design, home decoration space design, landscape space design, and historical arcade and building renovation design.