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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]

• The impact of influencing factors on injury severity of side crashes at intersections and role of striking and struck vehicles are investigated. • Bayesian bivariate probit model is proposed to accommodate the correlation between the injury severity levels of striking and struck vehicles, and Bayesian approach via Markov Chain Monte Carlo (MCMC) is employed to estimate the model. • The Bayesian bivariate probit model can effectively address the correlation and provide better model fit than Bayesian univariate probit model. • The findings can help designers and management departments improve the safety at intersections. Side crashes between vehicles which usually lead to high casualties and property loss, rank first among total crashes in China. This paper aims to identify the factors associated with injury severity of side crashes at intersections and to provide suggestions for developing countermeasures to mitigate the levels of injuries. In order to investigate the role of striking and struck vehicles in side crashes simultaneously, bivariate probit model was proposed and Bayesian approach was employed to evaluate the model, compared to the corresponding univariate probit model. Crash data from Beijing, China for the period 2009–2012 were used to carry out the statistical analysis. Based on the investigation with vehicles and data analysis on events, 130 intersection side crash cases were selected to form a specific dataset. Then, the influence of human, vehicles, roadway and environmental variables on crash severity was examined by means of bivariate probit regression within Bayesian framework. The effects of the factors on striking vehicle drivers and struck vehicle drivers were considered separately and simultaneously to find more targeted conclusions. The statistical analysis revealed vehicle type, lane number, no non-motorized lane and speeding have the corresponding influence on the injury severity of striking vehicles, while time of day and vehicle type of struck vehicles increased the likelihood of being injured. From the results it can be concluded that there indeed exists correlation between striking and struck vehicles in side crashes, although the correlation is not so strong. Importantly, Bayesian bivariate probit model can address the role of striking and struck vehicles in side crashes simultaneously and can accommodate the correlation clearly, which extends the range of univariate probit analysis. The general and empirical countermeasures are presented to improve the safety at intersections. [ABSTRACT FROM AUTHOR]