A Review of Road Defect Recognition Based on Unmanned Aerial Vehicles and Deep Learning
Main Article Content
Abstract
Real-time monitoring of transportation infrastructure is crucial for ensuring the safety and operational efficiency of road networks. Traditional manual inspections involve high risks and low efficiency, whereas unmanned aerial vehicles (UAVs), with their high maneuverability and broad coverage, have gradually become an important data collection platform in the field of pavement defect detection. However, the large-scale application of UAVs in complex environments still faces technical bottlenecks such as environmental interference, inconsistent data quality, and slow processing of massive images. This paper systematically reviews the frontier progress of UAV-based pavement defect monitoring technologies in recent years: First, it outlines the limitations and challenges during the UAV field data collection phase; Second, it deeply analyzes the algorithmic evolution in office data processing, from image preprocessing and 3D pavement reconstruction to deep learning and semantic segmentation; Building upon this, the paper constructs a multi-dimensional evaluation system ranging from apparent 2D defects to hidden 3D defects and summarizes multi-modal fusion recognition methods, including visible light, LiDAR, infrared thermal imaging, and ground-penetrating radar (GPR). Finally, the paper discusses the limitations of current technologies regarding hardware battery life, legal privacy, and complex background interference, and points out the development potential of edge computing, UAV swarm collaboration, and cyber-physical systems in future intelligent road maintenance.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Serhani, M. A.; Ng, T. T.; Al Falasi, A.; Saedi, M. A.; Nuaimi, F. A.; Shamsi, H. A.; Shamsi, A. D. A. Drone-Assisted Inspection for Automated Accident Damage Estimation: A Deep Learning Approach. In 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN); 2019; pp 682–687. https://doi.org/10.1109/ICUFN.2019.8806100.
Ahmed, A.; Outay, F.; Zaidi, S. O. R.; Adnan, M.; Ngoduy, D. Examining Queue-Jumping Phenomenon in Heterogeneous Traffic Stream at Signalized Intersection Using UAV-Based Data. Pers Ubiquit Comput 2021, 25 (1), 93–108. https://doi.org/10.1007/s00779-020-01434-y.
Barmpounakis, E.; Geroliminis, N. On the New Era of Urban Traffic Monitoring with Massive Drone Data: The pNEUMA Large-Scale Field Experiment. Transportation Research Part C: Emerging Technologies 2020, 111, 50–71. https://doi.org/10.1016/j.trc.2019.11.023.
Wu, W.; Qurishee, M. A.; Owino, J.; Fomunung, I.; Onyango, M.; Atolagbe, B. Coupling Deep Learning and UAV for Infrastructure Condition Assessment Automation. In 2018 IEEE International Smart Cities Conference (ISC2); 2018; pp 1–7. https://doi.org/10.1109/ISC2.2018.8656971.
Tan, Y.; Li, Y. UAV Photogrammetry-Based 3D Road Distress Detection. ISPRS International Journal of Geo-Information 2019, 8 (9), 409. https://doi.org/10.3390/ijgi8090409.
Dobson, R. J.; Brooks, C.; Roussi, C.; Colling, T. Developing an Unpaved Road Assessment System for Practical Deployment with High-Resolution Optical Data Collection Using a Helicopter UAV. In 2013 International Conference on Unmanned Aircraft Systems (ICUAS); 2013; pp 235–243. https://doi.org/10.1109/ICUAS.2013.6564695.
Congress, S. S. C.; Puppala, A. J. Evaluation of UAV–CRP Data for Monitoring Transportation Infrastructure Constructed over Expansive Soils. Indian Geotech J 2020, 50 (2), 159–171. https://doi.org/10.1007/s40098-019-00384-4.
Peddinti, P. R. T.; Kim, B. Efficient Pavement Monitoring for South Korea Using Unmanned Aerial Vehicles. 2022, 61–72. https://doi.org/10.1061/9780784484357.006.
Hassanalian, M.; Abdelkefi, A. Classifications, Applications, and Design Challenges of Drones: A Review. Progress in Aerospace Sciences 2017, 91, 99–131. https://doi.org/10.1016/j.paerosci.2017.04.003.
Karak, A.; Abdelghany, K. The Hybrid Vehicle-Drone Routing Problem for Pick-up and Delivery Services. Transportation Research Part C: Emerging Technologies 2019, 102, 427–449. https://doi.org/10.1016/j.trc.2019.03.021.
Gao, M.; Hugenholtz, C. H.; Fox, T. A.; Kucharczyk, M.; Barchyn, T. E.; Nesbit, P. R. Weather Constraints on Global Drone Flyability. Sci Rep 2021, 11 (1), 12092. https://doi.org/10.1038/s41598-021-91325-w.
Rao, B.; Gopi, A. G.; Maione, R. The Societal Impact of Commercial Drones. Technology in Society 2016, 45, 83–90. https://doi.org/10.1016/j.techsoc.2016.02.009.
Torija, A. J.; Li, Z.; Self, R. H. Effects of a Hovering Unmanned Aerial Vehicle on Urban Soundscapes Perception. Transportation Research Part D: Transport and Environment 2020, 78, 102195. https://doi.org/10.1016/j.trd.2019.11.024.
da Silveira, F.; Lermen, F. H.; Amaral, F. G. An Overview of Agriculture 4.0 Development: Systematic Review of Descriptions, Technologies, Barriers, Advantages, and Disadvantages. Computers and Electronics in Agriculture 2021, 189, 106405. https://doi.org/10.1016/j.compag.2021.106405.
Peddinti, P. R. T.; Puppala, H.; Kim, B. Pavement Monitoring Using Unmanned Aerial Vehicles: An Overview. J. Transp. Eng., Part B: Pavements 2023, 149 (3), 03123002. https://doi.org/10.1061/JPEODX.PVENG-1291.
Manjusha, M.; Sunitha, V. A Review of Advanced Pavement Distress Evaluation Techniques Using Unmanned Aerial Vehicles. Int. J. Pavement Eng. 2023, 24 (2). https://doi.org/10.1080/10298436.2023.2268796.
Roberts, R.; Inzerillo, L.; Di Mino, G. Using UAV Based 3D Modelling to Provide Smart Monitoring of Road Pavement Conditions. Information 2020, 11 (12), 568. https://doi.org/10.3390/info11120568.
Roberts, R.; Giancontieri, G.; Inzerillo, L.; Di Mino, G. Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning. Applied Sciences 2020, 10 (1), 319. https://doi.org/10.3390/app10010319.
Claros Zelaya, R. A.; Guevara Aguilar, A. E.; Pacas Cruz, N. R. Aplicación de fotogrametría aérea en levantamientos topográficos mediante el uso de vehículos aéreos no tripulados. 2016.
Romero-Chambi, E.; Villarroel-Quezada, S.; Atencio, E.; Muñoz-La Rivera, F. Analysis of Optimal Flight Parameters of Unmanned Aerial Vehicles (UAVs) for Detecting Potholes in Pavements. Applied Sciences 2020, 10 (12), 4157. https://doi.org/10.3390/app10124157.
Atencio, E.; Plaza-Muñoz, F.; Muñoz-La Rivera, F.; Lozano-Galant, J. A. Calibration of UAV Flight Parameters for Pavement Pothole Detection Using Orthogonal Arrays. Automation in Construction 2022, 143, 104545. https://doi.org/10.1016/j.autcon.2022.104545.
Congress, S. NOVEL INFRASTRUCTURE MONITORING USING MULTIFACETED UNMANNED AERIAL VEHICLE SYSTEMS - CLOSE RANGE PHOTOGRAMMETRY (UAV - CRP) DATA ANALYSIS. Civil Engineering Dissertations-Archive 2018.
Congress, S. S. C.; Puppala, A. J.; Kumar, P.; Patil, U. D. Assessment of Pavement Geometric Characteristics Using UAV-CRP Data. 2021, 332–343. https://doi.org/10.1061/9780784483534.029.
Goodbody, T. R. H.; Coops, N. C.; White, J. C. Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions. Curr Forestry Rep 2019, 5 (2), 55–75. https://doi.org/10.1007/s40725-019-00087-2.
James, M. R.; Robson, S.; d’Oleire-Oltmanns, S.; Niethammer, U. Optimising UAV Topographic Surveys Processed with Structure-from-Motion: Ground Control Quality, Quantity and Bundle Adjustment. Geomorphology 2017, 280, 51–66. https://doi.org/10.1016/j.geomorph.2016.11.021.
James, M. R.; Robson, S. Mitigating Systematic Error in Topographic Models Derived from UAV and Ground-Based Image Networks. Earth Surface Processes and Landforms 2014, 39 (10), 1413–1420. https://doi.org/10.1002/esp.3609.
Nesbit, P. R.; Hugenholtz, C. H. Enhancing UAV–SfM 3D Model Accuracy in High-Relief Landscapes by Incorporating Oblique Images. Remote Sensing 2019, 11 (3), 239. https://doi.org/10.3390/rs11030239.
Micheletti, N.; Chandler, J.; Lane, S. Structure from Motion (SFM) Photogrammetry; 2015.
Turner, D.; Lucieer, A.; Wallace, L. Direct Georeferencing of Ultrahigh-Resolution UAV Imagery. IEEE Transactions on Geoscience and Remote Sensing 2014, 52 (5), 2738–2745. https://doi.org/10.1109/TGRS.2013.2265295.
Tsai, M. L.; Chiang, K. W.; Huang, Y. W.; Lin, Y. S.; Tsai, J. S.; Lo, C. F.; Lin, Y. S.; Wu, C. H. The Development of a Direct Georeferencing Ready UAV Based Photogrammetry Platform.
Rippin, D. M.; Pomfret, A.; King, N. High Resolution Mapping of Supra-Glacial Drainage Pathways Reveals Link between Micro-Channel Drainage Density, Surface Roughness and Surface Reflectance. Earth Surface Processes and Landforms 2015, 40 (10), 1279–1290. https://doi.org/10.1002/esp.3719.
Inzerillo, L.; Acuto, F.; Di Mino, G.; Uddin, M. Z. Super-Resolution Images Methodology Applied to UAV Datasets to Road Pavement Monitoring. Drones 2022, 6 (7), 171. https://doi.org/10.3390/drones6070171.
Xiang, C.; Wang, W.; Deng, L.; Shi, P.; Kong, X. Crack Detection Algorithm for Concrete Structures Based on Super-Resolution Reconstruction and Segmentation Network. Automation in Construction 2022, 140, 104346. https://doi.org/10.1016/j.autcon.2022.104346.
Bae, H.; Jang, K.; An, Y.-K. Deep Super Resolution Crack Network (SrcNet) for Improving Computer Vision–Based Automated Crack Detectability in in Situ Bridges. Structural Health Monitoring 2021, 20 (4), 1428–1442. https://doi.org/10.1177/1475921720917227.
Xu, B.; Liu, C. Pavement Crack Detection Algorithm Based on Generative Adversarial Network and Convolutional Neural Network under Small Samples. Measurement 2022, 196, 111219. https://doi.org/10.1016/j.measurement.2022.111219.
Gao, Y.; Kong, B.; Mosalam, K. M. Deep Leaf-Bootstrapping Generative Adversarial Network for Structural Image Data Augmentation. Computer-Aided Civil and Infrastructure Engineering 2019, 34 (9), 755–773. https://doi.org/10.1111/mice.12458.
Lin, Z.; Wang, H.; Li, S. Pavement Anomaly Detection Based on Transformer and Self-Supervised Learning. Automation in Construction 2022, 143, 104544. https://doi.org/10.1016/j.autcon.2022.104544.
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; Uszkoreit, J.; Houlsby, N. An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv June 3, 2021. https://doi.org/10.48550/arXiv.2010.11929.
Structure from Motion Photogrammetric Technique. In Developments in Earth Surface Processes; Elsevier, 2020; Vol. 23, pp 1–24. https://doi.org/10.1016/B978-0-444-64177-9.00001-1.
Schönberger, J. L.; Frahm, J.-M. Structure-from-Motion Revisited. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016; pp 4104–4113. https://doi.org/10.1109/CVPR.2016.445.
Li, B.; Wang, K. C. P.; Zhang, A.; Yang, E.; Wang, G. Automatic Classification of Pavement Crack Using Deep Convolutional Neural Network. International Journal of Pavement Engineering 2020, 21 (4), 457–463. https://doi.org/10.1080/10298436.2018.1485917.
Mokhtari, S.; Wu, L.; Yun, H.-B. Comparison of Supervised Classification Techniques for Vision-Based Pavement Crack Detection. Transp. Res. Record 2016, No. 2595, 119–127. https://doi.org/10.3141/2595-13.
Hammer, B.; Mokbel, B.; Schleif, F.-M.; Zhu, X. White Box Classification of Dissimilarity Data. In Hybrid Artificial Intelligent Systems; Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B., Eds.; Springer: Berlin, Heidelberg, 2012; pp 309–321. https://doi.org/10.1007/978-3-642-28942-2_28.
Liu, J.; Yang, X.; Lau, S.; Wang, X.; Luo, S.; Lee, V. C.-S.; Ding, L. Automated Pavement Crack Detection and Segmentation Based on Two-Step Convolutional Neural Network. Comput.-Aided Civil Infrastruct. Eng. 2020, 35 (11), 1291–1305. https://doi.org/10.1111/mice.12622.
Amieghemen, G. E.; Sherif, M. M. Deep Convolutional Neural Network Ensemble for Pavement Crack Detection Using High Elevation UAV Images. Structure and Infrastructure Engineering 2025, 21 (6), 1008–1023. https://doi.org/10.1080/15732479.2023.2263441.
Liu, J.; Yang, X.; Lau, S.; Wang, X.; Luo, S.; Lee, V. C.-S.; Ding, L. Automated Pavement Crack Detection and Segmentation Based on Two-Step Convolutional Neural Network. Computer-Aided Civil and Infrastructure Engineering 2020, 35 (11), 1291–1305. https://doi.org/10.1111/mice.12622.
Cheng, H.; Zhang, J.; Yan, H.; Wang, Y.; Li, D. RLD-Net: An Enhanced Deep Learning Model for Accurate Pavement Distress Detection Using UAV Captured Images. Multiscale and Multidiscip. Model. Exp. and Des. 2025, 8 (8), 353. https://doi.org/10.1007/s41939-025-00950-9.
Zhang, C.; Nateghinia, E.; Miranda-Moreno, L. F.; Sun, L. Pavement Distress Detection Using Convolutional Neural Network (CNN): A Case Study in Montreal, Canada. International Journal of Transportation Science and Technology 2022, 11 (2), 298–309. https://doi.org/10.1016/j.ijtst.2021.04.008.
Yue, B.; Dang, J.; Sun, Q.; Wang, Y.; Min, Y.; Wang, F. TSPCS-Net: Two-Stage Pavement Crack Segmentation Network Based on Encoder-Decoder Architecture. Engineering Applications of Artificial Intelligence 2025, 141, 109840. https://doi.org/10.1016/j.engappai.2024.109840.
Zhong, J.; Zhu, J.; Huyan, J.; Ma, T.; Zhang, W. Multi-Scale Feature Fusion Network for Pixel-Level Pavement Distress Detection. Automation in Construction 2022, 141, 104436. https://doi.org/10.1016/j.autcon.2022.104436.
He, X.; Tang, Z.; Deng, Y.; Zhou, G.; Wang, Y.; Li, L. UAV-Based Road Crack Object-Detection Algorithm. Automation in Construction 2023, 154, 105014. https://doi.org/10.1016/j.autcon.2023.105014.
Liu, F.; Liu, J.; Wang, L. Deep Learning and Infrared Thermography for Asphalt Pavement Crack Severity Classification. Automation in Construction 2022, 140, 104383. https://doi.org/10.1016/j.autcon.2022.104383.
Famili, A.; Sarasua, W. A.; Shams, A.; Davis, W. J.; Ogle, J. H. Application of Mobile Terrestrial LiDAR Scanning Systems for Identification of Potential Pavement Rutting Locations. Transportation Research Record: Journal of the Transportation Research Board 2021, 2675 (9), 1063–1075. https://doi.org/10.1177/03611981211005777.
Wang, K. C. P.; Li, L.; Luo, W.; Larkin, A. Potential Measurement of Pavement Surface Texture Based on Three-Dimensional Image Data; 2012.
Liu, J.; Lemus-Romani, J.; Rueda, E. J.; Becerra-Rozas, M.; Astorga, G. Identification of Pathologies in Pavements by Unmanned Aerial Vehicle (UAV): A Systematic Literature Review. Drones 2026, 10 (2), 90. https://doi.org/10.3390/drones10020090.
Zuo, D.; Shao, L.; Chen, B.; Tan, J.; Yang, J. Pavement Damage Detection and Evaluation Based on UAV Image and Improved AHP. Case Studies in Construction Materials 2025, 22, e04169. https://doi.org/10.1016/j.cscm.2024.e04169.
Yang, H.; Jiang, F.; Wu, X.; Zhao, G.; Shi, X.; Liu, G.; Wang, M. Optimizing the Cutting Edge Geometry of Micro Drill Based on the Entropy Weight Method. Int J Adv Manuf Technol 2023, 125 (5), 2673–2689. https://doi.org/10.1007/s00170-023-10884-6.
Akbar, Muh.; Pamuttu, D. L.; Budianto, E.; Rachmat, R.; Mardiyadi, Z. Road Damage Identification Using a Combination of UAV Quadcopter Technology and Subgrade Investigation. IJSSE 2025, 15 (1), 79–85. https://doi.org/10.18280/ijsse.150109.
Fu, P.; Harvey, J. T.; Lee, J. N.; Vacura, P. New Method for Classifying and Quantifying Cracking of Flexible Pavements in Automated Pavement Condition Survey. Transp. Res. Record 2011, No. 2225, 99–108. https://doi.org/10.3141/2225-11.
Li, Y.; Liu, C.; Yue, G.; Gao, Q.; Du, Y. Deep Learning-Based Pavement Subsurface Distress Detection via Ground Penetrating Radar Data. Automation in Construction 2022, 142, 104516. https://doi.org/10.1016/j.autcon.2022.104516.
Todkar, S. S.; Le Bastard, C.; Ihamouten, A.; Baltazart, V.; Dérobert, X.; Fauchard, C.; Guilbert, D.; Bosc, F. Detection of Debondings with Ground Penetrating Radar Using a Machine Learning Method. In 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR); 2017; pp 1–6. https://doi.org/10.1109/IWAGPR.2017.7996056.
Zhang, W.; Li, X.; Wang, L.; Zhang, D.; Lu, P.; Wang, L.; Cheng, C. A Lightweight Method for Road Defect Detection in UAV Remote Sensing Images with Complex Backgrounds and Cross-Scale Fusion. Remote Sensing 2025, 17 (13), 2248. https://doi.org/10.3390/rs17132248.
Arya, D.; Maeda, H.; Ghosh, S. K.; Toshniwal, D.; Sekimoto, Y. RDD2022: A Multi-National Image Dataset for Automatic Road Damage Detection. Geosci. Data J. 2024, 11 (4), 846–862. https://doi.org/10.1002/gdj3.260.
Meier, J.; Welborn, E.; Diamantas, S. Pothole Segmentation and Area Estimation with Thermal Imaging Using Deep Neural Networks and Unmanned Aerial Vehicles. Machine Vision and Applications 2025, 36 (1), 17. https://doi.org/10.1007/s00138-024-01637-w.
He, K.; Gkioxari, G.; Dollár, P.; Girshick, R. Mask R-CNN. In 2017 IEEE International Conference on Computer Vision (ICCV); 2017; pp 2980–2988. https://doi.org/10.1109/ICCV.2017.322.
Outay, F.; Mengash, H. A.; Adnan, M. Applications of Unmanned Aerial Vehicle (UAV) in Road Safety, Traffic and Highway Infrastructure Management: Recent Advances and Challenges. Transportation Research Part A: Policy and Practice 2020, 141, 116–129. https://doi.org/10.1016/j.tra.2020.09.018.
Zhang, L.-Y.; Peng, Z.-R.; Sun, D. (Jian); Liu, X. A UAV-Based Automatic Traffic Incident Detection System for Low Volume Roads; 2013.
Araujo, T. M. A. de; Teixeira, C. A. de M.; Francês, C. R. L. Enhancing Geotechnical Damage Detection with Deep Learning: A Convolutional Neural Network Approach. PeerJ Comput. Sci. 2024, 10, e2052. https://doi.org/10.7717/peerj-cs.2052.
Zhang, W.; Li, H.; Li, Y.; Liu, H.; Chen, Y.; Ding, X. Application of Deep Learning Algorithms in Geotechnical Engineering: A Short Critical Review. Artif. Intell. Rev. 2021, 54 (8), 5633–5673. https://doi.org/10.1007/s10462-021-09967-1.
Chen, J.; Li, H.; Jiang, N.; Chen, Q.; Yang, Y.; Zhou, J. Stability Evaluation of Shallow Blocks in High and Steep Slope Combining TLS and UAV Photogrammetry. Geomatics, Natural Hazards and Risk 2025, 16 (1), 2464052. https://doi.org/10.1080/19475705.2025.2464052.
Xu, Q.; Zhao, B.; Dai, K.; Dong, X.; Li, W.; Zhu, X.; Yang, Y.; Xiao, X.; Wang, X.; Huang, J.; Lu, H.; Deng, B.; Ge, D. Remote Sensing for Landslide Investigations: A Progress Report from China. Engineering Geology 2023, 321, 107156. https://doi.org/10.1016/j.enggeo.2023.107156.
Mozaffari, M.; Saad, W.; Bennis, M.; Debbah, M. Wireless Communication Using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization. IEEE Trans. Wirel. Commun. 2017, 16 (12), 8052–8066. https://doi.org/10.1109/TWC.2017.2756644.
Nikitas, A.; Michalakopoulou, K.; Njoya, E. T.; Karampatzakis, D. Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era. Sustainability 2020, 12 (7). https://doi.org/10.3390/su12072789.
Hengstler, M.; Enkel, E.; Duelli, S. Applied Artificial Intelligence and Trust—The Case of Autonomous Vehicles and Medical Assistance Devices. Technological Forecasting and Social Change 2016, 105 (C), 105–120.
Du, Y.; Cheng, Q.; Liu, X.; Xu, J.; Yi, Y. Enhancing Road Maintenance Through Cyber-Physical Integration: The LEE-YOLO Model for Drone-Assisted Pavement Crack Detection. IEEE Trans. Intell. Transp. Syst. 2025, 26 (9), 14169–14178. https://doi.org/10.1109/TITS.2025.3540909.
Xiang, X.; Hu, H.; Ding, Y.; Zheng, Y.; Wu, S. GC-YOLOv5s: A Lightweight Detector for UAV Road Crack Detection. Appl. Sci.-Basel 2023, 13 (19). https://doi.org/10.3390/app131911030.
Chow, J. Y. J. Dynamic UAV-Based Traffic Monitoring under Uncertainty as a Stochastic Arc-Inventory Routing Policy. International Journal of Transportation Science and Technology 2016, 5 (3), 167–185. https://doi.org/10.1016/j.ijtst.2016.11.002.
Wu, D.; Arkhipov, D. I.; Kim, M.; Talcott, C. L.; Regan, A. C.; McCann, J. A.; Venkatasubramanian, N. ADDSEN: Adaptive Data Processing and Dissemination for Drone Swarms in Urban Sensing. IEEE Trans. Comput. 2016, 1–1. https://doi.org/10.1109/TC.2016.2584061.