Real-Time Pixel-Wise Segmentation of Road Surface Damage Using a 2D U-Net Architecture - Olzhayev, Olzhas; Kulambayev, Bakhytzhan; Omarov, Batyrkhan (Procedia computer science, 2025)
Road surface integrity is critical for ensuring vehicular safety and reducing maintenance costs, yet traditional visual inspections are laborious and subject to human error. This study presents a tailored 2D U-Net architecture for automated pixel-wise segmentation of pavement damage specifically cracks and potholes from smartphone-captured RGB imagery. The proposed model employs an encoder–decoder design with symmetric skip connections to merge high-level contextual information and fine-grained spatial features. A weighted cross-entropy loss function is used to mitigate severe class imbalance between sparse damage pixels and dominant intact surface areas. Training on a curated dataset of 1,000 labeled road images demonstrates steady convergence: training loss decreases from 2.65 to 2.25, while validation accuracy rises from 65 % to 76 % over 36 epochs. Qualitative results reveal that coarse crack clusters are reliably detected by epoch 20, with finer details emerging by epoch 30. Mobile deployment tests on a mid-range smartphone yield an average inference time of 45 ms per frame, and TensorFlow Lite quantization reduces model size by 30 % with less than 1 % IoU degradation. These findings confirm that the 2D U-Net framework balances segmentation accuracy and on-device efficiency. Future work will explore hybrid loss formulations and expanded datasets including nighttime and adverse weather conditions to further enhance generalization and robustness.