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Craquelure is the most common defect on ancient polychrome paintings, which may deteriorate further to paint loss. Previous image processing methods, which can accurately recognize paint loss, have limited precision and efficiency in segmenting craquelure. This paper proposes a semantic segmentation method, Res-UNet, for the recognition of craquelure and paint loss in the Palace Museum, Beijing. The residual structure of ResNet-50 enables the avoidance of network degradation, and image features can be fully extracted. Using the unique skip connection module of U-Net, features of different levels are fused to improve segmentation accuracy and provide smoother craquelure edges. Three loss functions are combined to accelerate stable convergence. The model was tested on a newly built dataset based on 600 images. Experimental results supported by statistical tests show that Res-UNet is a capable method of craquelure recognition, with an accuracy rate of 98.19%, and F1-score of 93.42%. Hence, the proposed hybrid approach is a promising tool to support the preservation and restoration of valuable traditional Chinese polychrome architectural paintings. [ABSTRACT FROM AUTHOR]

Urban expressway is the main artery of traffic network, and an in-depth analysis of the crashes is crucial for improving the traffic safety level of expressways. This study intended to address the injury severity of expressways in Beijing by proposing Bayesian ordered logistic regression model. Crash data were collected from urban express rings and expressways in 2015 and 2016. The results showed that crash location, time and crash season are significant variables influencing injury severity. The findings revealed that the proposed model can address the ordinal feature of injury severity, while accommodating the data with small sample sizes that may not adequately represent population characteristics. The conclusions can provide the management departments with valuable suggestions for the injury prevention and safety improvement on the urban expressways. [ABSTRACT FROM AUTHOR]