{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T22:05:16Z","timestamp":1768601116822,"version":"3.49.0"},"reference-count":35,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Asphalt roads play a vital role in land transportation systems, significantly contributing to the growth and development of economies and societies. However, over time, the quality of these roads deteriorates due to aging and the cumulative effects of wear and tear, leading to various pavement and road issues such as potholes, cracks, and damaged sidewalks. This paper aims to develop a deep learning model, specifically leveraging the YOLOv8 object detection framework, to detect and classify road infrastructure problems using images captured from unmanned aerial vehicles (UAVs). The model processes a series of road images from the Roboflow dataset and was trained on Google Colab, utilizing advanced machine learning techniques to analyze the images and accurately identify road damage. Subsequently, the model was evaluated using metrics such as accuracy, recall, precision, and F1-score. The results demonstrated that the model is both efficient and reliable. The model achieved high performance, with an F1-score of 94\u202f%, precision of 93\u202f%, and recall of 95\u202f%, which indicates its effectiveness in identifying various road defects. By detecting and locating issues such as potholes, cracks, and sidewalk damage, this model offers a promising solution for maintaining road infrastructure, supporting smart transportation systems, enhancing road safety, and helping reduce hazards and accidents.<\/jats:p>","DOI":"10.1515\/jisys-2025-0055","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T11:09:48Z","timestamp":1768561788000},"source":"Crossref","is-referenced-by-count":0,"title":["Automated detection and classification of road infrastructure for intelligent urban planning"],"prefix":"10.1515","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9092-0174","authenticated-orcid":false,"given":"Alaa","family":"Bafail","sequence":"first","affiliation":[{"name":"Faculty of Computing and Information Technology , King Abdulaziz University , Jeddah 21551 , Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3053-142X","authenticated-orcid":false,"given":"Dania","family":"Aljeaid","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology , King Abdulaziz University , Jeddah 21551 , Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0238-9051","authenticated-orcid":false,"given":"Hanan","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology , King Abdulaziz University , Jeddah 21551 , Saudi Arabia"}]},{"given":"Noor","family":"Bajunaid","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences , Prince Sultan University , Riyadh 12435 , Saudi Arabia"}]},{"given":"Renad","family":"Algarni","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology , King Abdulaziz University , Jeddah 21551 , Saudi Arabia"}]},{"given":"Njoud","family":"Almutairi","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology , King Abdulaziz University , Jeddah 21551 , Saudi Arabia"}]},{"given":"Ebtsam","family":"Alghamdi","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology , King Abdulaziz University , Jeddah 21551 , Saudi Arabia"}]}],"member":"374","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"2026011611094377208_j_jisys-2025-0055_ref_001","doi-asserted-by":"crossref","unstructured":"M. Guerrieri, G. Parla, M. Khanmohamadi, and L. Neduzha, \u201cAsphalt pavement damage detection through deep learning technique and cost-effective equipment: A case study in urban roads crossed by tramway lines,\u201d Infrastructures, vol.\u00a09, no. 2, p.\u00a034, 2024, https:\/\/doi.org\/10.3390\/infrastructures9020034.","DOI":"10.3390\/infrastructures9020034"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_002","doi-asserted-by":"crossref","unstructured":"L. Xu, K. Fu, T. Ma, F. Tang, and J. Fan, \u201cAutomatic detection of urban pavement distress and dropped objects with a comprehensive dataset collected via smartphone,\u201d Buildings, vol.\u00a014, no. 6, p.\u00a01546, 2024, https:\/\/doi.org\/10.3390\/buildings14061546.","DOI":"10.3390\/buildings14061546"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_003","doi-asserted-by":"crossref","unstructured":"C. Chu, L. Wang, and H. Xiong, \u201cA review on pavement distress and structural defects detection and quantification technologies using imaging approaches,\u201d J.\u00a0Traffic Transp. Eng. Engl. Ed., vol.\u00a09, no. 2, pp.\u00a0135\u2013150, 2022, https:\/\/doi.org\/10.1016\/j.jtte.2021.04.007.","DOI":"10.1016\/j.jtte.2021.04.007"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_004","doi-asserted-by":"crossref","unstructured":"D. V. Achillopoulou, S. A. Mitoulis, S. A. Argyroudis, and Y. Wang, \u201cMonitoring of transport infrastructure exposed to multiple hazards: A roadmap for building resilience,\u201d Sci. Total Environ., vol. 746, p. 141001, 2020. https:\/\/doi.org\/10.1016\/j.scitotenv.2020.141001.","DOI":"10.1016\/j.scitotenv.2020.141001"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_005","doi-asserted-by":"crossref","unstructured":"J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, \u201cYou only look once: Unified, real-time object detection,\u201d 2015, arXiv, https:\/\/doi.org\/10.48550\/ARXIV.1506.02640.","DOI":"10.1109\/CVPR.2016.91"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_006","unstructured":"A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, \u201cYOLOv4: Optimal speed and accuracy of object detection,\u201d 2020, arXiv, https:\/\/doi.org\/10.48550\/ARXIV.2004.10934."},{"key":"2026011611094377208_j_jisys-2025-0055_ref_007","unstructured":"G. Jocher, YOLOv5 by Ultralytics (Version 7.0) [Computer software] 2020, https:\/\/doi.org\/10.5281\/zenodo.3908559."},{"key":"2026011611094377208_j_jisys-2025-0055_ref_008","doi-asserted-by":"crossref","unstructured":"C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, \u201cYOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,\u201d 2022, arXiv: arXiv:2207.02696, https:\/\/doi.org\/10.48550\/arXiv.2207.02696.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_009","doi-asserted-by":"crossref","unstructured":"G. Wang, Y. Chen, P. An, H. Hong, J. Hu, and T. Huang, \u201cUAV-YOLOv8: A small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios,\u201d Sensors, vol.\u00a023, no. 16, p.\u00a07190, 2023, https:\/\/doi.org\/10.3390\/s23167190.","DOI":"10.3390\/s23167190"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_010","doi-asserted-by":"crossref","unstructured":"E. H. I. Eliwa and T. Abd El-Hafeez, \u201cAdvancing crop health with YOLOv11 classification of plant diseases,\u201d Neural Comput. 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Falkowski-Gilski, Eds., in Algorithms for Intelligent Systems, Singapore, Springer Nature Singapore, 2024, pp.\u00a0529\u2013545.","DOI":"10.1007\/978-981-99-7962-2_39"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_013","unstructured":"G. Jocher, A. Chaurasia, and J. Qiu, Ultralytics YOLOv8 (Version 8.0.0) [Computer software]. Ultralytics, 2023. Available at: https:\/\/github.com\/ultralytics\/ultralytics (AGPL-3.0 License)."},{"key":"2026011611094377208_j_jisys-2025-0055_ref_014","unstructured":"\u201cYOLOv8: State-of-the-art computer vision model.\u201d https:\/\/yolov8.com\/ [Accessed: Feb. 24, 2025]."},{"key":"2026011611094377208_j_jisys-2025-0055_ref_015","doi-asserted-by":"crossref","unstructured":"V. Mandal, L. Uong, and Y. Adu-Gyamfi, \u201cAutomated road crack detection using deep convolutional neural networks,\u201d in 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, IEEE, 2018, pp.\u00a05212\u20135215.","DOI":"10.1109\/BigData.2018.8622327"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_016","doi-asserted-by":"crossref","unstructured":"S.-S. Park, V.-T. Tran, and D.-E. Lee, \u201cApplication of various YOLO models for computer vision-based real-time pothole detection,\u201d Appl. Sci., vol.\u00a011, no. 23, p.\u00a011229, 2021, https:\/\/doi.org\/10.3390\/app112311229.","DOI":"10.3390\/app112311229"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_017","doi-asserted-by":"crossref","unstructured":"S. V. H. Pham and K. V. T. Nguyen, \u201cProductivity assessment of the Yolo V5 model in detecting road surface damages,\u201d Appl. Sci., vol.\u00a013, no. 22, p.\u00a022, 2023, https:\/\/doi.org\/10.3390\/app132212445.","DOI":"10.3390\/app132212445"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_018","doi-asserted-by":"crossref","unstructured":"F.-J. Du and S.-J. Jiao, \u201cImprovement of lightweight convolutional neural network model based on YOLO algorithm and its research in pavement defect detection,\u201d Sensors, vol.\u00a022, no. 9, p.\u00a03537, 2022, https:\/\/doi.org\/10.3390\/s22093537.","DOI":"10.3390\/s22093537"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_019","doi-asserted-by":"crossref","unstructured":"J. Pei, X. Wu, and X. 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Bao et al.., \u201cApplication of YOLO model in road defect detection,\u201d in 2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Nanjing, China, IEEE, 2024, pp.\u00a02280\u20132284.","DOI":"10.1109\/AINIT61980.2024.10581746"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_022","doi-asserted-by":"crossref","unstructured":"Z. Liu, W. Wu, X. Gu, S. Li, L. Wang, and T. Zhang, \u201cApplication of combining YOLO models and 3D GPR images in road detection and maintenance,\u201d Remote Sens., vol.\u00a013, no. 6, p.\u00a01081, 2021, https:\/\/doi.org\/10.3390\/rs13061081.","DOI":"10.3390\/rs13061081"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_023","doi-asserted-by":"crossref","unstructured":"J. Zhu, D. Zhao, and X. Luo, \u201cEvaluating the optimised YOLO-based defect detection method for subsurface diagnosis with ground penetrating radar,\u201d Road Mater. Pavement Des., vol.\u00a025, no. 1, pp.\u00a0186\u2013203, 2024, https:\/\/doi.org\/10.1080\/14680629.2023.2199880.","DOI":"10.1080\/14680629.2023.2199880"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_024","doi-asserted-by":"crossref","unstructured":"C. Ruseruka, J. Mwakalonge, G. Comert, S. Siuhi, F. Ngeni, and K. Major, \u201cPavement distress identification based on computer vision and controller area network (CAN) sensor models,\u201d Sustainability, vol.\u00a015, no. 8, p.\u00a06438, 2023, https:\/\/doi.org\/10.3390\/su15086438.","DOI":"10.3390\/su15086438"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_025","doi-asserted-by":"crossref","unstructured":"S. Cano-Ortiz, L. Lloret Iglesias, P. Martinez Ruiz Del \u00c1rbol, P. Lastra-Gonz\u00e1lez, and D. Castro-Fresno, \u201cAn end-to-end computer vision system based on deep learning for pavement distress detection and quantification,\u201d Constr. Build. Mater., vol. 416, p. 135036, 2024. https:\/\/doi.org\/10.1016\/j.conbuildmat.2024.135036.","DOI":"10.1016\/j.conbuildmat.2024.135036"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_026","doi-asserted-by":"crossref","unstructured":"M. A. Benallal and M. S. Tayeb, \u201cAn image-based convolutional neural network system for road defects detection,\u201d IAES Int. J.\u00a0Artif. Intell. IJ-AI, vol.\u00a012, no. 2, p.\u00a0577, 2023, https:\/\/doi.org\/10.11591\/ijai.v12.i2.pp577-584.","DOI":"10.11591\/ijai.v12.i2.pp577-584"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_027","doi-asserted-by":"crossref","unstructured":"J. Dharneeshkar, S. A. Aniruthan, R. Karthika, and L. Parameswaran, \u201cDeep learning based detection of potholes in Indian roads using YOLO,\u201d in Proc. 2020 Int. Conf. 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Sarker, \u201cImproved YOLOv5-based real-time road pavement damage detection in road infrastructure management,\u201d Algorithms, vol.\u00a016, no. 9, p.\u00a0452, 2023, https:\/\/doi.org\/10.3390\/a16090452.","DOI":"10.3390\/a16090452"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_030","unstructured":"PD, Road Defect (MobileNetSSDV2) Computer Vision Model [Open Source Dataset], Roboflow Universe, 2023, Available at: https:\/\/universe.roboflow.com\/pd-vl3mk\/road-defect-mobilenetssdv2."},{"key":"2026011611094377208_j_jisys-2025-0055_ref_031","unstructured":"Project-jumiq, bigproject Dataset [Open source dataset], Roboflow Universe. Roboflow, 2023, Available at: https:\/\/universe.roboflow.com\/project-jumiq\/bigproject-ymkh7."},{"key":"2026011611094377208_j_jisys-2025-0055_ref_032","unstructured":"\u201cGoogle colab.\u201d https:\/\/colab.research.google.com\/ [Accessed: Feb. 24, 2025]."},{"key":"2026011611094377208_j_jisys-2025-0055_ref_033","unstructured":"H. Priyanto, N. Syakrani, M. R. Sholahuddin, T. Gelar, and R. Tubagus. \u201cYOLOv8 to YOLO11: A comprehensive architecture in-depth comparative review.\u201d arXiv preprint arXiv:2501.13400. 2025, https:\/\/doi.org\/10.48550\/arXiv.2501.13400."},{"key":"2026011611094377208_j_jisys-2025-0055_ref_034","doi-asserted-by":"crossref","unstructured":"R. Mirajkar, A. Yenkikar, S. Nawalkar, R. Kaul, A. Rokade, and K. Rothe, \u201cEnhanced pothole detection in road condition assessment using YOLOv8,\u201d in 2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE), Bangalore, India, IEEE, 2024, pp.\u00a0429\u2013433.","DOI":"10.1109\/ICWITE59797.2024.10502437"},{"key":"2026011611094377208_j_jisys-2025-0055_ref_035","unstructured":"Z. Mohd Shah and M. Mohd, \u201cReal-time pothole detection using deep learning,\u201d Evol. Electr. Electron. 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