Abstract
This study presents a multi-scale ROI-aligned deep learning framework designed to advance automated road damage detection and severity assessment using high-resolution roadway imagery. The proposed architecture integrates hierarchical feature extraction, a road-damage proposal network, and refined ROI-aligned encoding to capture both fine-grained local anomalies and broader contextual patterns across diverse pavement conditions. Leveraging the RDD2020 dataset, the model effectively identifies multiple defect categories, including longitudinal cracks, transverse cracks, alligator cracking, and potholes, achieving strong convergence behavior and stable generalization across training and validation phases. Quantitative evaluations reveal high detection accuracy and smooth loss reduction over 500 learning epochs, while qualitative visualizations demonstrate precise localization and robust classification of damages under varying environmental and structural complexities. The framework consistently maintains performance in challenging scenes featuring shadows, cluttered backgrounds, low contrast, or irregular defect geometries, underscoring the benefits of multi-scale fusion and ROI alignment mechanisms. Although slight fluctuations in validation metrics indicate the presence of inherently difficult samples, the overall results affirm the model’s capability to support large-scale, real-time road monitoring systems. The findings highlight the potential of the proposed approach to significantly enhance intelligent transportation infrastructure, offering an efficient and reliable solution for proactive pavement maintenance and improved roadway safety.