(untitled) (Revista Científica UCOM Scientia, 2025)
This article presents the development of an automatic crack segmentation system for asphalt pavement using convolutional neural networks, specifically the U-Net architecture. A proprietary dataset was built with 847 images captured in the city of Coronel Oviedo, Paraguay, of which 505 contained cracks. The images were manually labeled and processed at a resolution of 256x256 pixels. The model was trained for 500 epochs using the binary crossentropy loss function and the Adam optimizer. Outstanding performance metrics were obtained, with an F1 Score of 0.9956 and an Intersection over Union (IoU) index of 0.9913. Additionally, post-segmentation processing was integrated for crack width quantification. In summary, these results demonstrate the high precision and robustness of the developed system.