A UAV Photography–Based Detection Method for Defective Road Marking
Publication: Journal of Performance of Constructed Facilities
Volume 36, Issue 5
Abstract
Markings are essential elements of roads and are important for driving safety enforcement. Traditional detection of marking defects relies on manual inspection, which is less effective. However, few studies could be found focusing on automatic detection of defective road markings. In this study, a detection method for defective road marking is proposed based on unmanned aerial vehicle (UAV) photography. Image acquisition based on UAV photography was discussed. The detailed processes of image preprocessing, road segmentation, marking extraction, and defective marking detection are presented. The proposed method was tested with images collected from highways, normal urban roads, and poorly maintained urban roads in Nanjing, China. Overall, the proposed method shows good robustness in defective marking detection. The precision and recall of marking extraction on highways and normal urban roads are over 93%, and the results on poorly maintained urban roads are about 76%, due to the poor road conditions. The precision of damage identification on lane markings and arrow markings were all greater than 90%. The proposed automatic process is capable of accurately and quantitatively detecting the sidelines, lane markings, and indicator markings.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported in part by the National Key R&D Project of China (Grant No. 2021YFB2600603), National Key R&D Program of China (Grant No. 2021YFB2600600), and Fundamental Research Funds for the Central Universities (Grant No. 2242022R10060).
References
Alzraiee, H., A. L. Ruiz, and R. Sprotte. 2021. “Detecting of pavement marking defects using faster R-CNN.” J. Perform. Constr. Facil. 35 (4): 04021035. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001606.
Chun, P., T. Yamane, and Y. Tsuzuki. 2021. “Automatic detection of cracks in asphalt pavement using deep learning to overcome weaknesses in images and GIS visualization.” Appl. Sci. 11 (3): 892. https://doi.org/10.3390/app11030892.
Ciptasari, R. W., K. H. Rhee, and K. Sakurai. 2013. “Exploiting reference images for image splicing verification.” Digital Invest. 10 (3): 246–258. https://doi.org/10.1016/j.diin.2013.06.014.
Deo, C., K. Emmanuel, and O. Mbakisya. 2018. “Evaluating the service life of thermoplastic pavement markings: Stochastic approach.” J. Transp. Eng. Part B Pavements 144 (3): 04018029. https://doi.org/10.1061/JPEODX.0000055.
Du, Z., J. Yuan, F. Xiao, and C. Hettiarachchi. 2021. “Application of image technology on pavement distress detection: A review.” Meas. J. Int. Meas. Confederation 184 (Nov): 109900. https://doi.org/10.1016/j.measurement.2021.109900.
Han, C., T. Ma, J. Huyan, X. Huang, and Y. Zhang. 2021. “CrackW-Net: A novel pavement crack image segmentation convolutional neural network.” IEEE Trans. Intell. Transp. Syst. 1–10. https://doi.org/10.1109/TITS.2021.3095507.
Huang, M., Q. Dong, F. Ni, and L. Wang. 2021. “LCA and LCCA based multi-objective optimization of pavement maintenance.” J. Cleaner Prod. 283 (Feb): 124583. https://doi.org/10.1016/j.jclepro.2020.124583.
Kawano, M., S. Yokoyama, T. Yonezawa, and N. Jin. 2017. “Road marking blur detection with drive recorder.” In Proc., 2017 IEEE Int. Conf. on Big Data (Big Data). New York: IEEE.
Li, H., and F. Nashashibi. 2011. “Robust real-time lane detection based on lane mark segment features and general a priori knowledge.” In 2011 IEEE Int. Conf. on Robotics and Biomimetics, 812–817. New York: IEEE. https://doi.org/10.1109/ROBIO.2011.6181387.
Liu, Y., X. Zhang, L. Liu, and L. Zhang. 2020. “Upsampling matters for road marking segmentation of autonomous driving.” IFAC-PapersOnLine 53 (5): 232–237. https://doi.org/10.1016/j.ifacol.2021.04.102.
Lowe, D. G. 2004. “Distinctive image features from scale-invariant keypoints.” Int. J. Comput. Vision 60 (2): 91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94.
Migletz, J., and J. L. Graham. 2002. “Transportation research board.” In NCHRP synthesis 306 long-term pavement marking practices. Washington, DC: National Academy Press.
Nancy, L. R. 1975. Gerpho: Groupe d’examen routier par photographie, appareil a grand rendement. Paris: Ministère de l’équipement.
National Highway Traffic Safety Administration. 2003. Traffic safety facts 2003: A compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system. Washington, DC: National Highway Traffic Safety Administration, USDOT.
Otsu, N. 1979. “A threshold selection method from gray-histogram.” IEEE Trans. Syst. Man Cybern. 9 (1): 62–66. https://doi.org/10.1109/TSMC.1979.4310076.
Sun, J. Y., S. W. Kim, S. W. Lee, Y. W. Kim, and S. J. Ko. 2019. “Reverse and boundary attention network for road segmentation.” In Proc., 2019 IEEE/CVF Int. Conf. on Computer Vision Workshop (ICCVW). New York: IEEE.
Sun, T. Y., S. J. Tsai, and V. Chan. 2006. “HSI color model-based lane-marking detection.” In Proc., 2006 IEEE Intelligent Transportation Systems Conf. New York: IEEE.
Tang, F., T. Ma, J. Zhang, Y. Guan, and L. Chen. 2020. “Integrating three-dimensional road design and pavement structure analysis based on BIM.” Autom. Constr. 113 (May): 103152. https://doi.org/10.1016/j.autcon.2020.103152.
Tao, C., J. Qi, Y. Li, H. Wang, and H. Li. 2019. “Spatial information inference net: Road extraction using road-specific contextual information.” ISPRS J. Photogramm. Remote Sens. 158 (Dec): 155–166. https://doi.org/10.1016/j.isprsjprs.2019.10.001.
Vokhidov, H., H. Hong, J. Kang, T. Hoang, and K. Park. 2016. “Recognition of damaged arrow road markings by visible light camera sensor based on convolutional neural network.” Sensors 16 (12): 2160. https://doi.org/10.3390/s16122160.
Wu, P., C. Chang, and C. H. Lin. 2014. “Lane-mark extraction for automobiles under complex conditions.” Pattern Recognit. 47 (8): 2756–2767. https://doi.org/10.1016/j.patcog.2014.02.004.
Xiao, D., X. Yang, J. Li, and M. Islam. 2020. “Attention deep neural network for lane marking detection.” Knowl.-Based Syst. 194 (Apr): 105584. https://doi.org/10.1016/j.knosys.2020.105584.
Ye, X. Y., D. S. Hong, H. H. Chen, P. Y. Hsiao, and L. C. Fu. 2020. “A two-stage real-time YOLOv2-based road marking detector with lightweight spatial transformation-invariant classification.” Image Vision Comput. 102 (Oct): 103978. https://doi.org/10.1016/j.imavis.2020.103978.
Zhong, M., L. Sui, Z. Wang, and D. Hu. 2020. “Pavement crack detection from mobile laser scanning point clouds using a time grid.” Sensors (Basel) 20 (15): 4198. https://doi.org/10.3390/s20154198.
Zhou, H., and M. P. Rouholamin. 2014. Guidelines for reducing wrong-way crashes on freeways. FHWA-ICT-14-010. Springfield, IL: Illinois Center for Transportation, Illinois DOT.
Zhu, J., J. Zhong, T. Ma, X. Huang, W. Zhang, and Y. Zhou. 2022. “Pavement distress detection using convolutional neural network with images captured via UAV.” Autom. Constr. 133 (Jan): 103991. https://doi.org/10.1016/j.autcon.2021.103991.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 36 • Issue 5 • October 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Oct 4, 2021
Accepted: Apr 18, 2022
Published online: Jun 24, 2022
Published in print: Oct 1, 2022
Discussion open until: Nov 24, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Zhipeng Wang, Junqing Zhu, Tao Ma, Deep Learning–Based Detection of Vehicle Axle Type with Images Collected via UAV, Journal of Transportation Engineering, Part B: Pavements, 10.1061/JPEODX.PVENG-1524, 150, 3, (2024).
- Junqing Zhu, Tianxiang Bu, Tao Ma, Xiaoming Huang, Feng Chen, Raster-Based Point Cloud Mapping of Defective Road Marking: Toward Automated Road Inspection via Airborne LiDAR, Journal of Transportation Engineering, Part B: Pavements, 10.1061/JPEODX.PVENG-1410, 150, 2, (2024).
- Pranav R. T. Peddinti, Harish Puppala, Byungmin Kim, Pavement Monitoring Using Unmanned Aerial Vehicles: An Overview, Journal of Transportation Engineering, Part B: Pavements, 10.1061/JPEODX.PVENG-1291, 149, 3, (2023).
- Seunghyeon Wang, Mincheol Kim, Hyeonyong Hae, Mengqiu Cao, Juhyung Kim, The Development of a Rebar-Counting Model for Reinforced Concrete Columns: Using an Unmanned Aerial Vehicle and Deep-Learning Approach, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-13686, 149, 11, (2023).
- Chendong Zhu, Junqing Zhu, Tianxiang Bu, Xiaofei Gao, Monitoring and Identification of Road Construction Safety Factors via UAV, Sensors, 10.3390/s22228797, 22, 22, (8797), (2022).
- Wanyue Kong, Teng Zhong, Xin Mai, Shuliang Zhang, Min Chen, Guonian Lv, Automatic Detection and Assessment of Pavement Marking Defects with Street View Imagery at the City Scale, Remote Sensing, 10.3390/rs14164037, 14, 16, (4037), (2022).
- Jingtao Zhong, Junqing Zhu, Ju Huyan, Tao Ma, Weiguang Zhang, Multi-scale feature fusion network for pixel-level pavement distress detection, Automation in Construction, 10.1016/j.autcon.2022.104436, 141, (104436), (2022).