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ارزیابی توانایی روش فتوگرامتری پهپاد در آشکارسازی ترک روسازی جاده‌ها | ||
| نشریه مهندسی عمران امیرکبیر | ||
| دوره 54، شماره 5، مرداد 1401، صفحه 1705-1730 اصل مقاله (2.75 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2021.19815.7263 | ||
| نویسندگان | ||
| سید آریا فخری* 1؛ محمد سعادت سرشت2؛ مسعود ورشوساز3؛ حمزه ذاکری4 | ||
| 1دانشگاه اصفهان | ||
| 2دانشیار دانشکده مهندسی نقشه‌برداری و اطلاعات مکانی، دانشگاه تهران | ||
| 3دانشیار دانشکده مهندسی نقشه‌برداری، دانشگاه صنعتی خواجه‌نصیرالدین طوسی | ||
| 4hafez | ||
| چکیده | ||
| به منظور ایجاد سامانه مدیریت روسازی راه‌ها باید مؤلفه‌های اطلاعاتی بر اساس دیدگاه‌های مختلف مدیریت روسازی اخذ شوند. از جمله مهم‌ترین مؤلفه‌های اطلاعاتی در این سامانه‌ها، ارزیابی کیفی وضعیت روسازی راه‌ها می‌باشد. لذا در این رابطه داده‌های حاوی جزئیات خرابی‌های سطحی روسازی از اهمیت فراوانی برخوردار است. خرابی‌های روسازی علاوه بر اینکه باعث استهلاک وسایل نقلیه، صرف هزینه‌های تعمیر و نگهداری و کاهش عمر مفید سازه روسازی می‌شوند، موجب بروز تصادفات و کاهش ایمنی معابر نیز می‌گردند. با توجه به اینکه مهم‌ترین خرابی‌های سطحی در روسازی جاده‌ها مربوط به ترک‌ها می‌باشند و از آنجایی که تشخیص این ترک‌ها به صورت بصری قابل‌برداشت و ارزیابی است، پس روش‌های مبتنی بر تصویربرداری می‌تواند در تشخیص آن بسیار کارگشا باشد. در این پژوهش از فتوگرامتری پهپاد به عنوان روشی مکمل جهت آشکارسازی خرابی‌های ناشی از ترک روسازی استفاده شده است. لذا با ارائه روندی جهت بررسی پارامترهای بهینه در طراحی شبکه فتوگرامتری پهپاد و همچنین پیاده‌سازی الگوریتمی خودکار مبتنی بر پردازش تصویر و طبقه‌بندی کننده درخت تصمیم‌گیری اقدام گردید. نتایج حاصل از ارزیابی صورت گرفته در خصوص آشکارسازی پیکسل‌های ترک توسط الگوریتم پیشنهادی و مقایسه آن با واقعیت زمینی در بهترین حالت و با استفاده از ارتوفتوموزائیک تولید شده از تصاویر اخذ شده توسط پهپاد فانتوم 4 پرو در ارتفاع 20 متری، دقت 96% حاصل شد. با توجه به دقت بالای این روش در آشکارسازی خرابی‌های ناشی از ترک، استفاده از فتوگرامتری پهپاد به عنوان روشی مکمل و قابل اعتماد در سامانه مدیریت روسازی پیشنهاد شده است. | ||
| کلیدواژه‌ها | ||
| سامانه مدیریت روسازی؛ ترک‌های روسازی؛ فتوگرامتری پهپاد؛ یادگیری ماشین و درخت تصمیم‌گیری | ||
| موضوعات | ||
| انواع خرابی روسازی انعطاف پذیر؛ مدیریت روسازی | ||
| عنوان مقاله [English] | ||
| Evaluation of UAV Photogrammetric capability in Road Pavement Cracks Detection | ||
| نویسندگان [English] | ||
| Seyed Arya Fakhri1؛ Mohammad Saadatseresht2؛ Masood Varshosaz3؛ HAMZEH ZAKERI4 | ||
| 1دانشجو دکتری | ||
| 2Department of Engineering, University of Tehran, Tehran, Iran | ||
| 3Department of Engineering, K. N. Toosi University of Technology, Tehran, Iran | ||
| 4RESEARCHER /AUT | ||
| چکیده [English] | ||
| To establish a system for managing road pavement, it is mandatory to prepare information components based on various perspectives of pavement management. One of the most significant information components in these systems is quality assessment regarding road pavement status. Accordingly, in this regard, data containing details of surface pavement failures and defects are of great significance. Apart from causing vehicle depreciation & damage, maintenance costs, and reducing the useful lifespan of the pavement structure, road pavement failures also lead to accidents and reduce road safety. Bearing in mind that the most important surface damages in road pavement are related to cracks with longitudinal, transverse, oblique, alligator, and block types, and as such cracks and defects can be visually and non-destructively assessed and evaluated, imaging-based approaches and techniques can provide details such as the type of defect, its severity, extent, and location and prove to be highly useful. In this paper, the Unmanned Aerial Vehicle (UAV)/drone photogrammetry has been proposed as a complementary approach aimed at providing information on defects caused by cracks in the country's road pavement management system. According to the author, the output of UAV photogrammetric products will significantly improve if the system parameters are adjusted. Consequently, through presenting a procedure to investigate the optimal parameters in the design of a UAV photogrammetric network, further, attempts were made for the implementation of an automated algorithm based on image processing operations & classifier decision tree which is independent of scale and image dimensions. Hence, after removing the road edges and determining the asphalt area, a pixel detection operation was carried out to reveal the cracks. Furthermore, after preparing the ground reality through selected orthophoto mosaic, the evaluation of crack pixel detection was determined using the proposed algorithm with three methods. An accuracy of 96% was determined for the main orthophoto mosaic. For the test orthophotos, which were the result of images taken by Phantom 4 Pro and Mavic Pro at different altitudes, an accuracy of approximately 82% to 91% was determined. | ||
| کلیدواژه‌ها [English] | ||
| PMS, UAV, Image Processing, Machine Learning, Decision tree | ||
| مراجع | ||
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[1] A. Safikhani, J. Salahshour, H. Dashtinaserabadi, The effect of using pavement management method on reducing the cost of annual road maintenance (in Persian), in: First National Conference on Infrastructure Engineering and Management, University of Tehran, 2009. [2] M. Ameri, B. Golchin, Familiarity with the concepts of pavement management system (in Persian), Ministry of Roads and Transportation, Deputy Minister of Research and Technology Education, Transportation Research Institute, Tehran, 2005. [3] R. Alinasab, evaluation of lime fillers effects on reduction of moisture and freeze damage by indirect tensile strength and compressive strength tests %J Modares Civil Engineering journal, 14(20) (2014) 77-85. [4] H. Ramezanpour, M. Ameri, S. Novbakht, Study and comparison of road procedure evaluation methods and the possibility of using these methods in evaluating the country's roads according to the available facilities (in Persian), University of Science and Industry, Tehran, 2001. [5] R. Haas, W.R. Hudson, J.P. Zaniewski, Modern pavement management, 1994. [6] W.R. Hudson, R. Haas, R.D.J.N.r. Pedigo, Pavement management system development, (215) (1979). [7] J.R. Mbwana, M.A.J.T.r.r. Turnquist, Optimization modeling for enhanced network-level pavement management system, 1524(1) (1996) 76-85. [8] C.L. Saraf, K.J.T.R.R. Majidzadeh, Distress prediction models for a network-level pavement management system, (1344) (1992). [9] S. Horton, Project level pavement management system development, (1990). [10] J.A. Reigle, Development of an integrated project-level pavement management model using risk analysis, (2000). [11] M. Safarzadeh, A. Kavousi, M.B. Sari, Provide a model for road pavement management at the project level through hierarchical analysis (in Persian), Transportation Research Journal, (2) (2006). [12] H. Naderi, M. Zokaei, Automatic evaluation of asphalt pavement failures by image processing method (in Persian), in: Third International Conference on Applied Research in Civil Engineering, Architecture and Urban Management, Khajeh Nasir al-Din Tusi University of Technology, 2015. [13] K.H. McGhee, Automated pavement distress collection techniques, Transportation Research Board, 2004. [14] H. Zakeri, F. Moghadasnejad, Expert system for detecting pavement failure caused by cracks in civil and environmental (in Persian), Amirkabir University of Technology, Tehran, 2008. [15] S. Soufi, S.M. Karimi, M. Abbasghorbani, Automatic assessment of pavement surface failures using a road surface scanner (in Persian), in: The First National Conference on Road and Transportation Engineering, University of Gilan, 2017. [16] C.o. authors, Instructions for monitoring and technical control of maps and spatial information prepared using UAVs (in Persian), in: G.O.o.T.S.a. Control (Ed.), Country Planning and Budget Organization, Country Mapping Organization, Iran, 2016. [17] P.J. Grandsaert, Integrating pavement crack detection and analysis using autonomous unmanned aerial vehicle imagery, (2015). [18] Y. Pan, X. Zhang, M. Sun, Q.J.I.A.o.t.P. Zhao, Remote Sensing, S.I. Sciences, Object-Based and Supervised Detection of Potholes and Cracks from the Pavement Images Acquired by UAV, 42 (2017). [19] A.B. Ersoz, O. Pekcan, T. Teke, Crack identification for rigid pavements using unmanned aerial vehicles, in: IOP Conference Series: Materials Science and Engineering, 2017. [20] G. Leonardi, V. Barrile, R. Palamara, F. Suraci, G. Candela, 3D Mapping of Pavement Distresses Using an Unmanned Aerial Vehicle (UAV) System, in: International Symposium on New Metropolitan Perspectives, Springer, 2018, pp. 164-171. [21] F. Dadrasjavan, N. Zarrinpanjeh, A. Ameri, Automatic Crack Detection of Road Pavement Based on Aerial UAV Imagery, (2019). [22] L. Zhang, W. Xu, L. Zhu, X. Yuan, C. Zhang, Study on Pavement Defect Detection Based on Image Processing Utilizing UAV, in: Journal of Physics: Conference Series, IOP Publishing, 2019, pp. 042011. [23] S.A. Fakhri, M. Saadatseresht, M. Varshosaz, H. Zakeri, Application of UAV photogrammetry in obtaining qualitative road pavement information, in: The 7th National Conference on Applied Research in Civil Engineering, Architecture and Urban Management and the 6th Specialized Exhibition of Mass Builders of Housing and Construction in Tehran Province, undefined, 2020. [24] A. Cubero-Fernandez, F.J. Rodriguez-Lozano, R. Villatoro, J. Olivares, J.M.J.E.J.o.I. Palomares, V. Processing, Efficient pavement crack detection and classification, 2017(1) (2017) 1-11. [25] S. Mokhtari, L. Wu, H.-B. Yun, Comparison of supervised classification techniques for vision-based pavement crack detection, Transportation Research Record, 2595(1) (2016) 119-127. [26] P. Sheng, L. Chen, J. Tian, Learning-based road crack detection using gradient boost decision tree, in: 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, 2018, pp. 1228-1232. [27] S.A. Fakhri, S.A. Fakhri, M. Saadatseresht, Road Crack Detection Using Gaussian/prewitt Filter, 42 (2019) 371-377. [28] S. Loussaief, A. Abdelkrim, Machine learning framework for image classification, in: 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), IEEE, 2016, pp. 58-61. [29] F. Galton, Finger Prints Macmillan, in, London, 1892. [30] S.A. Fakhri, M. Saadatseresht, M. Varshosaz, H. Zakeri, Automatic Estimation of the Spatial Resolution Coefficient of UAV Images Based on Siemens Star Target (in Persian), Journal of Geomatics Science and Technology, 10(4) (2021) 191-204. | ||
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