One of the problems of infrastructure maintenance is the review of its quality, which controls the state of infrastructure, such as roads, bridges and similar objects. Cracks are a very early indicator of the possible deterioration of infrastructure objects, which can be dangerous for users. Fast and accurate detection of these cracks can reduce maintenance costs and improve efficiency. The diploma thesis presents a solution to this problem by applying supervised deep learning for detection of cracks on concrete surfaces. Additions to the solution are also presented, which significantly help to improve the efficiency and performance of the model. The solution was tested on several different image datasets and compared to related approaches.
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