| Summary: | Approaching to PDCA (Plan - Do - Check - Take Action) in management of infrastructure asset requires digital transformation, sufficient data and strong database supporting management, analysis as well as creation of data-driven decision making tools. For pavements, data including condition indicators such as roughness and rutting depth are collected automatically during the survey vehicle travelling. However, pavement crack ratio and crack features of pattern and segmentation have not been detected by the system but manual in the case in Vietnam. The paper presents result of research on algorithm of statistic machine learning model in AI applying deep learning algorithm to automatically detect crack feature on pavement photos for enhancement of the performance and productivity of current survey technology. In the research, a deep architecture using convolutional neural network (CNN) for crack segmentation on gray scale images has been developed. The results show the CNN model for crack segmentation is better than other methods using traditional digital processing such as the Gabor filters or threshold and machine learning such as Adaboost.
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