Estimating cost of pothole repair from digital images using Stereo Vision and Artificial Neural Network
DOI:
https://doi.org/10.58190/ijamec.2024.77Abstract
A significant amount of road maintenance cost goes into pothole repairs. The primary cost factors related to potholes are their size and depth, as larger and thicker potholes incur higher repair costs. However, existing methods for estimating pothole repair in developing countries rely on manual size measurements, which is time consuming, labor intensive, subjective and can lead to poor estimation of repair cost. This paper presents a system that can automatically determine the size of potholes from digital images and estimate the cost of repair.
In this study, the stereo vision method was used to automatically estimate the depths of potholes from digital camera images. A feed-forward backward propagation Artificial Neural Network (ANN) was trained using pothole images acquired using mobile phones. The predicted depths and sizes of the potholes were then used to estimate the quantity of materials required to fill the potholes and subsequently, the cumulative cost of repair. Marking out and manual size measurements were performed for twenty randomly selected potholes in the Ugbowo Campus of the University of Benin, Nigeria. These measurements were compared against the estimated sizes of potholes predicted by the ANN model. A system was developed to automatically compute these material costs and considering other cost components such as transportation, labor, and equipment.
Results obtained showed that the mean errors for depth, width and height estimates were 3.403%, 3.789% and 5.2617% respectively. Consequently, the developed system correctly estimated the cost of repair of the potholes considered in this study. A significant contribution of the paper is the speed and convenience of acquiring pothole data using a mobile phones without the need for on spot assessment of potholes or use of relatively more expensive stereoscopic camera setup.
Downloads
References
S. Chen, M. Kuhn, K. Prettner, and D. E. Bloom, “The global macroeconomic burden of road injuries: estimates and projections for 166 countries,” Lancet Planet. Health, vol. 3, no. 9, pp. e390–e398, 2019.
P. Sagar and P. Singh, “Criminalization of Pothole: A Need of the Hour,” Issue 2 Intl JL Mgmt Hum., vol. 4, p. 2716, 2021.
B. G. Shivaleelavathi, V. Yatnalli, S. Y. V. Chinmayi, and S. Thotad, “Design and development of an intelligent system for pothole and hump identification on roads,” Int J Recent Technol Eng, vol. 8, no. 3, pp. 5294–5300, 2019.
I. Yustiana, D. Gustian, A. P. Junfithrana, and S. K. Damodar, “Broken Road Detection Methods Comparison: A Literature Survey,” Int. J. Eng. Appl. Technol. IJEAT, vol. 5, no. 2, pp. 16–23, 2022.
S. H. Carpenter and T. P. Wilson, “Evaluations of Improved Cold-Mix Binders—Field Operations Plan,” Fed. Highw. Adm. Contract, no. DTFH61-90–00021, 1991.
FHWA, “Pothole Repair in Asphalt Concrete Pavements,” Federal Highway Administration, US Department of Transportation, Washington, DC, FHWA-RD-99-106, 1999.
J. Nicholls, D. Smith, and M. Sayers, “Assessment of Generic Pothole Repair Materials,” Transp. Res. Rec., vol. 2509, pp. 14-21, 2015.
A. Ipavec, “Durable Pothole Repairs,” Road Mater. Pavement Des., vol. 13, no. 4, pp. 483-502, 2012.
M. R. Al-Rifaie and A. A. Al-Amin, “Repair of Cracked and Delaminated Portholes Using Vacuum-Assisted Resin Injection,” Mar. Struct., vol. 32, no. 1, pp. 1-13, 2012.
M. R. Al-Rifaie and A. A. Al-Amin, “Repair of Cracked Portholes Using Composite Repair Patches,” Ocean Eng., vol. 115, pp. 21-29, 2015.
Sealmaster, “How to Fix a Pothole.” 2023. Accessed: Sep. 16, 2023. [Online]. Available: https://sealmaster.net/faq/how-to-fix-a-pothole/
L. D. Evans, “Materials and Procedures for Pavement Repairs—Final Report, National Research Council, Strategic Highway Research Program,” Contract SHRP-89-H-106, 1993.
K. T. Chang, J. R. Chang, and J. K. Liu, “Detection of pavement distress using 3D laser scanning technology,” in Proceedings of the ASCE International Conference on Computing in Civil Engineering, 2005, pp. 1–11.
Q. Li, M. Yao, X. Yao, and B. Xu, “A real-time 3D scanning system for pavement distortion inspection,” Meas. Sci. Technol., vol. 21, no. 1, p. 015702, 2009.
Z. Hou, K. C. P. Wang, and W. Gong, “Experimentation of 3D pavement imaging through stereovision,” in Proceedings of the International Conference on Transportation Engineering, 2007, pp. 376–381.
B. X. Yu and X. Yu, “Vibration-based system for pavement condition evaluation,” in Applications of advanced technology in transportation, 2006, pp. 183–189.
C. Koch and I. Brilakis, “Pothole detection in asphalt pavement images,” Adv. Eng. Inform., vol. 25, no. 3, pp. 507–515, 2011.
Y.-M. Kim, Y.-G. Kim, S.-Y. Son, S.-Y. Lim, B.-Y. Choi, and D.-H. Choi, “Review of recent automated pothole-detection methods,” Appl. Sci., vol. 12, no. 11, p. 5320, 2022.
N. K. B. Ramaiah and S. Kundu, “Stereo Vision Based Pothole Detection System for Improved Ride Quality,” SAE Int. J. Adv. Curr. Pract. Mobil., vol. 3, no. 2021-01–0085, pp. 2603–2610, 2021.
L. Ma, Y. Li, J. Li, C. Wang, R. Wang, and M. A. Chapman, “Mobile laser scanned point-clouds for road object detection and extraction: A review,” Remote Sens., vol. 10, no. 10, p. 1531, 2018.
Z. Zhang, X. Ai, C. K. Chan, and N. Dahnoun, “An efficient algorithm for pothole detection using stereo vision,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2014, pp. 564–568.
Y. Li, C. Papachristou, and D. Weyer, “Road pothole detection system based on stereo vision,” in NAECON 2018-IEEE National Aerospace and Electronics Conference, IEEE, 2018, pp. 292–297.
S. Arjapure and D. R. Kalbande, “Deep learning model for pothole detection and area computation,” in 2021 International Conference on Communication information and Computing Technology (ICCICT), IEEE, 2021, pp. 1–6.
S.-Y. Lee, T. H. M. Le, and Y.-M. Kim, “Prediction and detection of potholes in urban roads: Machine learning and deep learning based image segmentation approaches,” Dev. Built Environ., vol. 13, p. 100109, 2023.
A. A. Fahmy, “Stereo vision-based depth estimation algorithm in uncelebrated rectification,” in Proceedings of the 13th International Journal of Video & Image Processing and Network Security (IJVIPNS-IJENS, 2010.
E. Salari, E. Chou, and J. J. Lynch, “Pavement distress evaluation using 3D depth information from stereo vision,” Michigan Ohio University Transportation Center, 2012.
C. Koch, G. M. Jog, and I. Brilakis, “Automated pothole distress assessment using asphalt pavement video data,” J. Comput. Civ. Eng., vol. 27, no. 4, pp. 370–378, 2013.
J. Erikson, L. Girod, and B. Hull, “The pothole patrol: Using a mobile sensor network for road surface monitoring,” in Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services, 2008, pp. 29–39.
B. U. Umar, M. A. Aibinu, O. M. Olaniyi, and E. Olaye, “Intelligent Cattle Detection and Recognition System Using ANN-Fourier Descriptor Techniques,” J. Comput. Sci. Its Appl., vol. 28, no. 2, pp. 143–154, 2021.
A. Shah et al., “A comprehensive study on skin cancer detection using artificial neural network (ANN) and convolutional neural network (CNN),” Clin. EHealth, vol. 6, pp. 76–84, 2023, doi: https://doi.org/10.1016/j.ceh.2023.08.002.
N. Wang, L. Shang, and X. Song, “A Transformer-Optimized Deep Learning Network for Road Damage Detection and Tracking,” Sensors, vol. 23, no. 17, p. 7395, 2023.
B. Grum et al., “Applicability and Cost Implication of Labor-Based Methods for Sustainable Road Maintenance (SRM) in Developing Countries,” Adv. Civ. Eng., vol. 2023, p. 1529148, Jun. 2023, doi: 10.1155/2023/1529148.
Md. S. Hossain, R. B. Angan, and M. M. Hasan, “Pothole Detection and Estimation of Repair Cost in Bangladeshi Street: AI-based Multiple Case Analysis,” in 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE), 2023, pp. 1–6. doi: 10.1109/ECCE57851.2023.10101579.
I. Moazzam, K. Kamal, S. Mathavan, S. Usman, and M. Rahman, “Metrology and visualization of potholes using the microsoft kinect sensor,” in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), IEEE, 2013, pp. 1284–1291.
S. H. Kumar, N. Kumar, and A. Barthwal, “Road Pothole Detection Using Smartphone Sensors,” J. Name, vol. 4, no. 5, pp. 47-51, 2023.
M. V. Thekkethala, R. S., S. J. Varughese, V. Mohan, and G. Titus, “Pothole Detection and Volume Estimation Using Stereoscopic Cameras,” Int. J. Ind. Electron. Electr. Eng., vol. 4, no. 5, pp. 47-51, 2016.
N. Ma et al., “Computer vision for road imaging and pothole detection: a state-of-the-art review of systems and algorithms,” Transp. Saf. Environ., vol. 4, no. 4, p. tdac026, 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
