Transport authorities rely on pavement characteristics to determine a pavement condition rating index. However, manually computing ratings can be a tedious, subjective, time-consuming, and training-intensive process. This paper presents a deep-learning framework for automatically rating the condition of rural road pavements using digital images captured from a dashboard-mounted camera. The framework includes pavement segmentation, data cleaning, image cropping and resizing, and pavement condition rating classification. A dataset of images, captured from diverse roads in Ireland and rated by two expert raters using the pavement surface condition index (PSCI) scale, was created. Deep-learning models were developed to perform pavement segmentation and condition rating classification. The automated PSCI rating achieved an average Cohen Kappa score and F1-score of 0.9 and 0.85, respectively, across 1–10 rating classes on an independent test set. The incorporation of unique image augmentation during training enabled the models to exhibit increased robustness against variations in background and clutter.
Qureshi, Waqar Shahid; Power, David; Ullah, Ihsan; Mulry, Brian; Feighan, Kieran; McKeever, Susan; and O'Sullivan, Dympna, "Deep Learning Framework For Intelligent Pavement Condition Rating: A direct classification approach for regional and local roads" (2023). Articles. 191.
Dr. Waqar S. Qureshi has received funding under the Marrie Currie Career-Fit plus postdoctoral fellowship program from Enterprise Ireland Grant No. M.F. 2021 0273, partially funded by the European Union's Horizon 2020 Research and Innovation Program under the Marie Sokolowski-Curie Co-funding of regional, national, and international programs Grant agreement No: 847402.
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