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Paper ID:IJSREM66754

RoadCrack Pro: Intelligent Road Damage Detection and Municipal Monitoring

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM66754

Volume:

10 |

8 |

Aug 2026

Pages:

1-20

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM66754

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KGK

Karan G K

Computer science

GM University

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PB

Prasad Bedage

Computer science

GM University

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Mentors

UN

Usha N

Computer science

GM University

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Abstract

Pavement defect detection is a critical component of modern-day road infrastructure and acts as a support system for the management of both human and freight transportation. The impact of daily vehicle traffic loads, variations in temperatures, rain, water intrusion and aging technology leads to progressive weakening of the pavement structure. Among many types of damage, cracks and potholes are most frequently encountered and can negatively influence vehicle safety and comfort of traveling. Leaving pavement failure unattended can lead to an 'irreversible' situation when damage on the road is significant. Typical road assessment is done through visual observation by engineers and municipal workers. Although visual observation is still implemented widely, it is costly, time-consuming and difficult to accomplish continuously across significant areas. Increasing urban road infrastructure triggers the need for automated methods of pavement assessment. Digital Image Processing (DIP) offers a useful means of automating the inspection of pavements. It is possible to process road pictures and enhance the crack structure, mask unwanted texture, remove damaged areas from the surrounding surface, and diagnose defects. However, road pictures may contain uneven light, shadowing, lane signs, background items, and rough texture, making it difficult to achieve accurate results. RoadCrack Pro helps solve this problem using a series of image processing techniques. The proposed scheme uses gray-scale and HSV conversion, COLOR and ROI masking, median and Gaussian filtering, bottom-hat morphological processing, adaptive thresholding, Sobel verification, morphological processing, and CC processing. The use of the system improves the detection process so that it includes pothole identification, severity detection, and municipality monitoring.

KEYWORDS

Digital Image Processing (DIP) HSV conversion COLOR and ROI masking median and Gaussian filtering

Cite This Article

Karan G K, Prasad Bedage, Usha N, "RoadCrack Pro: Intelligent Road Damage Detection and Municipal Monitoring," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 8, pp. 1-20, Aug 2026. https://doi.org/10.55041/IJSREM66754

Full Article/Conclusion

The RoadCrack Pro system is a way to use Digital Image Processing to find damage on the road. It uses techniques like ROI masking and image filtering to find the damaged areas. The system can also find potholes. Combine all the damage it finds into one map. Then it tells us how bad the damage is and what we should do to fix it. We tested the system on roads that were damaged and roads that were not. On the damaged road the system found that 9.41 percent of the road was damaged. It was also very accurate with a Precision of 86.9 percent a Recall of 69.8 percent an F1-score of 77.4 percent and an IoU of 63.2 percent. On the road that was not damaged the system said the road was in condition, with no damage at all. The new system for watching over the city helps store and look at information from road checks. Gives a basis for making decisions about maintenance. RoadCrack Pro is a way to keep an eye on the condition of roads because it looks at damage to the pavement figures out how bad it is, suggests repairs and manages checks. This system is not perfect because it has problems with light the texture of the pavement and choosing the settings and it needs to be tested more.. It shows that using computers to look at pictures can work for checking the pavement. If we add information use special computer programs add GPS take pictures with phones and use the internet to watch over the roads we can make the system better make it work for more roads and make it easier to use. So RoadCrack Pro shows how we can use computer programs to watch over the pavement and make decisions, about repairs and it is easy to understand and does not need a lot of power.

References

[1] I. H. Abbas and M. Q. Ismael, “Automated pavement distress detection using image processing techniques,” Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7702–7708, 2021. [2] Y. Safyari, M. Mahdianpari, and H. Shiri, “A review of vision-based pothole detection methods using computer vision and machine learning,” Sensors, vol. 24, no. 17, Art. no. 5652, 2024, doi: 10.3390/s24175652. [3] H. Maeda, Y. Sekimoto, T. Seto, T. Kashiyama, and H. Omata, “Road damage detection and classification using deep neural networks with smartphone images,” Computer-Aided Civil and Infrastructure Engineering, vol. 33, pp. 1127–1141, 2018, doi: 10.1111/mice.12387. [4] Q. Zou, Z. Zhang, Q. Li, X. Qi, Q. Wang, and S. Wang, “DeepCrack: Learning hierarchical convolutional features for crack detection,” IEEE Transactions on Image Processing, vol. 28, no. 3, pp. 1498–1512, 2019, doi: 10.1109/TIP.2018.2878966. [5] Q. Zou, Y. Cao, Q. Li, Q. Mao, and S. Wang, “CrackTree: Automatic crack detection from pavement images,” Pattern Recognition Letters, vol. 33, no. 3, pp. 227–238, 2012, doi: 10.1016/j.patrec.2011.11.004. [6] H. Oliveira and P. L. Correia, “Automatic road crack detection and characterization,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 155–168, 2013, doi: 10.1109/TITS.2012.2208630. [7] H. Oliveira and P. L. Correia, “CrackIT—An image processing toolbox for crack detection and characterization,” in Proc. IEEE Int. Conf. Image Processing (ICIP), 2014, pp. 798–802. [8] M. Avila, S. Begot, F. Duculty, and T. S. Nguyen, “2D image based road pavement crack detection by calculating minimal paths and dynamic programming,” in Proc. IEEE Int. Conf. Image Processing (ICIP), 2014, pp. 783–787. [9] R. Amhaz, S. Chambon, J. Idier, and V. Baltazart, “A new minimal path selection algorithm for automatic crack detection on pavement images,” in Proc. IEEE Int. Conf. Image Processing (ICIP), 2014, pp. 788–792. [10] X. Wang and Z. Hu, “Grid-based pavement crack analysis using deep learning,” in Proc. 4th Int. Conf. Transportation Information and Safety (ICTIS), 2017, pp. 917–924, doi: 10.1109/ICTIS.2017.8047878.

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Publication Date

8/14/2026

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Review Rounds: 2

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