Abstract
Roads are inevitable parts of human civilisation, and construction of roads are considered under a Civil Engineering problem; but periodically these roads require maintenance and assessment, which is highly dependent on adequate and timely pavement condition data. Howbeit, in some cases, it has been found that the manual practice of collecting and analysing such data often leads to delay in reporting about the issues and fixing them on time. Also, repairing potholes is time consuming, and locating these manually is a huge task. We want to find out some mechanism which can identify the construction conditions as well as any kind of deformities on the road from the dashboard camera fitted into a car, and at the same time, can analyse the conditions of road surface and formation of potholes on the road. Optimization of manual pothole detection through automation has been a part of scientific research since long. Pothole identification has significantly been adapted in different screening and maintenance systems. But in our country, owing to the large number of road networks and wide variations in the nature of rural and urban road conditions, it is very difficult to identify potholes through an automated system. In this paper, we have looked into several methods of Computer Vision, like image processing techniques and object detection method so as to identify potholes from the video input stream to the system. But these techniques have been found to have different challenges like lighting conditions, interference in the line of vision on waterlogged roads, and inefficiency at night vision. Hence, furthermore, we have explored the viability of Deep Learning method for identifying the potholes from the processing of input video streams, and have also analysed the Convolutional Neural Networks approach of Deep Learning through a self-built CNN model. In this paper, the expediency of all the methods as well as their drawbacks have been discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kim, T., Ryu, S.-K.: Review and analysis of pothole detection methods. J. Emerging Trends Comput. Inf. Sci. 5(8), 603–608 (2014)
Mednis, A., Strazdins, G., Zviedris, R., Kanonirs, G., Selavo, L.: Real time pothole detection using android smartphones with accelerometers. In: 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS), pp. 1–6. IEEE (2011)
Murthy, S.B.S., Varaprasad, G.: Detection of potholes in autonomous vehicle. IET Intel. Transport Syst. 8(6), 543–549 (2014)
Hegde, S., Mekali, H.V., Varaprasad, G.: Pothole detection and inter vehicular communication. In: 2014 IEEE International Conference on Vehicular Electronics and Safety, pp. 84–87. IEEE (2014)
Venkatesh, S., Abhiram, E., Rajarajeswari, S., Sunil Kumar, K.M., Balakuntala, S., Jagadish, N.: An intelligent system to detect, avoid and maintain potholes: a graph theoretic approach. In: 2014 Seventh International Conference on Mobile Computing and Ubiquitous Networking (ICMU), pp. 80–80. IEEE (2014)
Azhar, K., Murtaza, F., Yousaf, M.H., Habib, H.A.: Computer vision based detection and localization of potholes in asphalt pavement images. In: 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–5. IEEE (2016)
Tiwari, S., Bhandari, R., Raman, B.: Roadcare: a deep-learning based approach to quantifying road surface quality. In: Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies, pp. 231–242 (2020)
Nienaber, S., Booysen, M.J., Kroon, R.S.: Detecting potholes using simple image processing techniques and real-world footage (2015)
Bello-Salau, H., Aibinu, A.M., Onwuka, E.N., Dukiya, J.J., Onumanyi, A.J.: Image processing techniques for automated road defect detection: a survey. In: 2014 11th International Conference on Electronics, Computer and Computation (ICECCO), pp. 1–4. IEEE (2014)
Xie, G., Wen, L.: Image edge detection based on opencv. Int. J. Electron. Electrical Eng. 1(2), 104–6 (2013)
Buza, E., Omanovic, S., Huseinovic, A.: Pothole detection with image processing and spectral clustering. In: Proceedings of the 2nd International Conference on Information Technology and Computer Networks, vol. 810, pp. 4853 (2013)
Amit, Y.: 2D Object Detection and Recognition: Models, Algorithms, and Networks. MIT Press (2002)
Viola, P., Jones, M., et al.: Robust real-time object detection. Int. J. Comput. Vision 4(34–47), 4 (2001)
Topal, C., Akınlar, C., Genç, Y.: Edge drawing: a heuristic approach to robust real-time edge detection. In: 2010 20th International Conference on Pattern Recognition, pp. 2424–2427. IEEE (2010)
Ravi, V., Rajendra Prasad, Ch., Sanjay Kumar, S., Ramchandar Rao, P.: Image enhancement on opencv based on the tools: Python 2.7
Lee, S.W., Kim, S., Han, J., An, K.E., Ryu, S.-K., Seo, D.: Experiment of image processing algorithm for efficient pothole detection. In: 2019 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–2. IEEE (2019)
Pereira, V., Tamura, S., Hayamizu, S., Fukai, H.: A deep learning-based approach for road pothole detection in timor leste. In: 2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pp. 279–284. IEEE (2018)
Munoz-Organero, M., Ruiz-Blaquez, R., Sánchez-Fernández, L.: Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving. Comput. Environ. Urban Syst. 68, 1–8 (2018)
Chandan, G., Jain, A., Jain, H., et al.: Real time object detection and tracking using deep learning and opencv. In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 1305–1308. IEEE (2018)
Lim Kuoy Suong and Jangwoo Kwon: Detection of potholes using a deep convolutional neural network. J. UCS 24(9), 1244–1257 (2018)
Chen, H., Yao, M., Gu, Q.: Pothole detection using location-aware convolutional neural networks. Int. J. Mach. Learn. Cybernet. 11(4), 899–911 (2020). https://doi.org/10.1007/s13042-020-01078-7
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Acharjee, C., Singhal, S., Deb, S. (2020). Machine Learning Approaches for Rapid Pothole Detection from 2D Images. In: Kar, N., Saha, A., Deb, S. (eds) Trends in Computational Intelligence, Security and Internet of Things. ICCISIoT 2020. Communications in Computer and Information Science, vol 1358. Springer, Cham. https://doi.org/10.1007/978-3-030-66763-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-66763-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66762-7
Online ISBN: 978-3-030-66763-4
eBook Packages: Computer ScienceComputer Science (R0)