The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management.

Automated crack segmentation is a prerequisite for scalable, objective condition assessment of civil infrastructure, yet conventional methods rely exclusively on visual features and remain vulnerable to low‐contrast surfaces, shadows, and background clutter encountered in routine inspections. This study introduces a text‐guided vision–language segmentation framework in which short, inspection‐style descriptions of crack type and surface condition, the kind of contextual knowledge inspectors already record, are used to condition pixel‐level prediction. Feature‐wise linear modulation (FiLM) aligns multiscale visual features with these linguistic descriptors, and an adaptive multiscale refinement module (AMRF) preserves crack boundary continuity under challenging surface conditions. To support this line of research, a multimodal dataset is contributed by annotating an existing crack mask repository with descriptive textual labels. On a unified benchmark combining seven public crack datasets, the proposed method achieves an F1 score of 79.37% and an intersection over union (IoU) of 65.80% when inspection descriptions are available, the highest among the convolutional, transformer, and foundation‐model baselines considered. It also shows a favorable precision–recall balance, faster training convergence, and qualitatively sharper feature activations in low‐contrast regions. All validation is limited to these seven public datasets; agency‐collected images and physical crack measurements were not evaluated. Beyond benchmark accuracy, the paper describes how the resulting masks can be converted into crack width, length, and density, which are inputs to established condition‐rating and maintenance‐prioritization workflows, and discusses deployment of the framework as an offline analysis module within existing inspection programs.