The increasing volume of vehicle traffic and climate change significantly impact the performance of road infrastructure, necessitating comprehensive analyses throughout the road lifecycle to ensure its resilience. While
traditional visual inspections remain prevalent for road assessment, they are hampered by high costs and subjective biases. Additionally, concrete pavement presents specific evaluation challenges due to its high stiffness
and susceptibility to cracking, spalling, and faulting, requiring precise detection techniques. In response to these challenges, deep data-based systems emerge as a promising solution. This research introduces a novel system for
detecting, locating, and quantifying damages in concrete pavement by combining convolutional neural networks with classical computer vision techniques. The system studies various CNNs and ultimately selects UNet ResNext101 for its superior performance. Additionally, the system applies perspective transformations, Hough Transform, and thresholding techniques to enhance feature extraction and improve damage quantification precision. This
combination mitigates the high data requirements typically associated with neural networks alone. By limiting the inspection area to specific slabs, the system improves efficiency. It is trained and tested using high-resolution
images from the LanammeUCR. This innovative approach could significantly transform the maintenance and monitoring processes of road infrastructure, leading to safer and more reliable transportation networks.