Real-Time Pixel-Wise Segmentation of Road Surface Damage Using a 2D U-Net Architecture
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 cra...
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| Vydané v: | Procedia computer science Ročník 269; s. 131 - 139 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
2025
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| ISSN: | 1877-0509, 1877-0509 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Kulambayev, Bakhytzhan Olzhayev, Olzhas Omarov, Batyrkhan |
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| Cites_doi | 10.1016/j.autcon.2025.106120 10.1109/TITS.2024.3360263 10.1109/TAFE.2023.3332495 10.3390/s23010053 10.14569/IJACSA.2024.01509107 10.1016/j.aei.2025.103262 10.1109/JIOT.2024.3466390 10.1109/IFSA-SCIS.2017.8023299 10.1109/TITS.2024.3509140 10.1088/1742-6596/2278/1/012020 10.1080/10298436.2023.2219366 10.17654/EC016040801 10.14569/IJACSA.2024.0150333 10.1177/14759217211053776 10.1080/14680629.2024.2374863 10.32604/cmc.2024.057213 10.1007/s12524-024-01963-6 10.14569/IJACSA.2023.0140979 10.22630/MGV.2024.33.3.4 10.32604/cmc.2022.029544 10.1016/j.autcon.2024.105899 10.1016/j.conbuildmat.2023.133582 10.1177/03611981251322484 10.1016/j.autcon.2022.104436 10.1109/TITS.2024.3405995 |
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| Keywords | Deep learning semantic segmentation Pixel-wise segmentation Road damage 2D U-Net |
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