AI-Driven Computer Vision for Pavement Distress Classification and Structural Degradation Mapping in Urban Highway Networks
DOI:
https://doi.org/10.51903/30q9ch55Keywords:
Artificial Intelligence, Computer Vision, Geographic Information Systems, Pavement Condition Index, Pavement Distress DetectionAbstract
Timely pavement condition assessment is essential for maintaining road safety and optimizing maintenance planning; however, conventional inspection methods remain labor-intensive, time-consuming, and often lack the capability for continuous large-scale monitoring. This study aims to develop an artificial intelligence-based pavement monitoring framework that integrates real-time pavement distress detection, Pavement Condition Index (PCI) estimation, and Geographic Information System (GIS)-based spatial visualization into a unified decision-support platform. The proposed framework employs a YOLO-based deep learning model for automated detection of multiple pavement distress types using vehicle-mounted camera imagery, followed by PCI computation and geospatial degradation mapping to support network-level pavement assessment. Experimental evaluation demonstrated that the proposed model achieved a composite precision of 0.923, recall of 0.881, mean Average Precision (mAP) of 0.769, and an average inference time of 8.4 ms, indicating its capability to perform accurate real-time pavement distress detection under diverse urban road conditions. The novelty of this research lies in the integration of AI-based pavement distress detection, automated PCI estimation, and GIS-based structural degradation mapping within a single low-cost vehicle-mounted inspection framework, enabling simultaneous condition assessment and spatial infrastructure management without relying on expensive inspection technologies. This integrated workflow provides a practical contribution by supporting maintenance prioritization, reducing inspection time, and improving data-driven pavement management for transportation agencies. The results demonstrate that the proposed framework offers an effective and scalable solution for intelligent pavement monitoring while extending the application of computer vision beyond object detection toward comprehensive infrastructure condition assessment and maintenance decision support.
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Alexis, R., & Huereca, C. (2025). Valorization of Industrial Ash Waste as Eco-Friendly Binder for Pavement Applications: Experimental Study on Strength and Permeability Properties. Civil Engineering Science and Technology, 1(2), 110–134. https://doi.org/10.51903/fd9z5952
Alsahfi, T. (2024). Spatial and Temporal Analysis of Road Traffic Accidents in Major Californian Cities Using a Geographic Information System. ISPRS International Journal of Geo-Information, 13(5), 157. https://doi.org/10.3390/ijgi13050157
Bai, Y., Quan, W., Shi, X., Yan, Z., & Yuan, G. (2026). A Review of UAV-Based Crack Detection in Civil Infrastructure: A Multi-Level Visual Analysis Framework, Scene Adaptability, and Challenges. Remote Sensing, 18(11), 1806. https://doi.org/10.3390/rs18111806
Chen, J., Zou, Y., & Shu, X. (2026). Planning Shaded Corridors to Mitigate Heat: Assessment of Solar Radiation Exposure of Cyclists and Its Relationship with Built Environment in Shanghai. Land, 15(5), 739. https://doi.org/10.3390/land15050739
Değer Şitilbay, B., & Yılmaz, M. O. (2026). Bridging Image-Based Detection and Field Evaluation: A Semi-Automated Pavement Distress Assessment Framework. Sustainability, 18(10), 4935. https://doi.org/10.3390/su18104935
Famewo, B. G., & Shokouhian, M. (2025). A Review of Pavement Performance Deterioration Modeling: Influencing Factors and Techniques. Symmetry, 17(11), 1992. https://doi.org/10.3390/sym17111992
Gagliardi, V., Tosti, F., Bianchini Ciampoli, L., Battagliere, M. L., D’Amato, L., Alani, A. M., & Benedetto, A. (2023). Satellite Remote Sensing and Non-Destructive Testing Methods for Transport Infrastructure Monitoring: Advances, Challenges and Perspectives. Remote Sensing, 15(2), 418. https://doi.org/10.3390/rs15020418
Igwenagu, U. T., Debnath, R., Ahmed, A. A., & Alam, M. J. (2025). An Integrated Approach for Earth Infrastructure Monitoring Using UAV and ERI: A Systematic Review. Drones, 9(3), 225. https://doi.org/10.3390/drones9030225
Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K.-H., Cho, Y. J., Noh, D. H., & Chung, S.-O. (2024). Nutrient Stress Symptom Detection in Cucumber Seedlings Using Segmented Regression and a Mask Region-Based Convolutional Neural Network Model. Agriculture, 14(8), 1390. https://doi.org/10.3390/agriculture14081390
Joumblat, R., Al Basiouni Al Masri, Z., Al Khateeb, G., Elkordi, A., El Tallis, A. R., & Absi, J. (2023). State-of-the-Art Review on Permanent Deformation Characterization of Asphalt Concrete Pavements. Sustainability, 15(2), 1166. https://doi.org/10.3390/su15021166
Li, S., Wang, S., & Wang, P. (2023). A Small Object Detection Algorithm for Traffic Signs Based on Improved YOLOv7. Sensors, 23(16), 7145. https://doi.org/10.3390/s23167145
Liu, J., Lemus-Romani, J., Rueda, E. J., Becerra-Rozas, M., & Astorga, G. (2026). Identification of Pathologies in Pavements by Unmanned Aerial Vehicle (UAV): A Systematic Literature Review. Drones, 10(2), 90. https://doi.org/10.3390/drones10020090
Liu, X., & Yin, C. (2025). 3D Reconstruction of Asphalt Pavement Macro-Texture Based on Convolutional Neural Network and Monocular Image Depth Estimation. Applied Sciences, 15(9), 4684. https://doi.org/10.3390/app15094684
Lomandu, V. (2026). Climate-Resilient Urban Infrastructure Development through Integrated Hydrological Analysis and Architectural Planning. Jurnal Rekayasa Sipil Dan Arsitektur, 2(1), 19–37. https://doi.org/10.51903/eqdpkr54
López-González, P. J., Reyes-González, D., Moreno-Vázquez, O., Vivar-Ocampo, R., Zamora-Castro, S. A., Santos Cortés, L. D., Trujillo-García, B. S., & Sangabriel-Lomelí, J. (2026). Inspection and Evaluation of Urban Pavement Deterioration Using Drones: Review of Methods, Challenges, and Future Trends. Future Transportation, 6(1), 10. https://doi.org/10.3390/futuretransp6010010
Louzi, N., AlJamal, M., & Al-Jamal, M. Q. (2026). A Novel Simulation-Based Framework for Predicting Lane-Level Pavement Deterioration Under Freight Loading and Stop-and-Go Urban Traffic. Infrastructures, 11(7), 219. https://doi.org/10.3390/infrastructures11070219
Lv, Z., Hao, Z., Zhu, Y., & Lu, C. (2025). A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress. Applied Sciences, 15(11), 6112. https://doi.org/10.3390/app15116112
Meftah, I., Hu, J., Asham, M. A., Meftah, A., Zhen, L., & Wu, R. (2024). Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning. Sensors, 24(5), 1647. https://doi.org/10.3390/s24051647
Min, W., Lu, P., Liu, S., & Wang, H. (2025). A Review of Crack Sealing Technologies for Asphalt Pavement: Materials, Failure Mechanisms, and Detection Methods. Coatings, 15(7), 836. https://doi.org/10.3390/coatings15070836
Moretti, L., Palozza, L., & D’Andrea, A. (2024). Causes of Asphalt Pavement Blistering: A Review. Applied Sciences, 14(5), 2189. https://doi.org/10.3390/app14052189
Ou, Z., He, S., Bu, R., Wang, P., & Gong, G. (2026). MCGC-Net: A Text-Enhanced Geometry-Consistent Network for UAV-Based Road Crack Detection. Sensors, 26(11), 3487. https://doi.org/10.3390/s26113487
Shtayat, A., Obaidat, M. T., Al-Mistarehi, B., Bader, A., Moridpour, S., & Alahmad, S. (2025). Optimizing Road Pavement Assessment Using Advanced Image Processing Techniques. Sustainability, 17(6), 2473. https://doi.org/10.3390/su17062473
Theodorakopoulos, L., & Theodoropoulou, A. (2026). Big Data Analytics for Geospatial Decision-Making in Smart Cities: A Review of Spatial Data, GeoAI and Urban Digital Twins. ISPRS International Journal of Geo-Information, 15(7), 278. https://doi.org/10.3390/ijgi15070278
Thompson, A., Desai, J., & Bullock, D. M. (2025). Evaluation of Connected Vehicle Pavement Roughness Data for Statewide Needs Assessment. Infrastructures, 10(9), 248. https://doi.org/10.3390/infrastructures10090248
Trigka, M., & Dritsas, E. (2025). A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection. Sensors, 25(1), 214. https://doi.org/10.3390/s25010214
Wu, Q., Meng, S., & Zhao, J. (2025). Text-Grounded LLM-Assisted Design Rationale Interfaces: Turning Advertising Layout Metadata into Explainable UI/UX Decision Cards. International Journal of Graphic Design, 3(1), 216–240. https://doi.org/10.51903/ijgd.v3i1.3713
Zhou, L., Zhao, S., Wan, Z., Liu, Y., Wang, Y., & Zuo, X. (2024). MFEFNet: A Multi-Scale Feature Information Extraction and Fusion Network for Multi-Scale Object Detection in UAV Aerial Images. Drones, 8(5), 186. https://doi.org/10.3390/drones8050186
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