A real-time automatic pothole detection system using convolution neural networks

Research Article
Open access

A real-time automatic pothole detection system using convolution neural networks

Ricardo Bharat 1 , Abiodun M Ikotun 2 , Absalom E. Ezugwu 3* , Laith Abualigah 4* , Mohammad Shehab 5 , Raed Abu Zitar 6
  • 1 School of Computer Science, University of KwaZulu-Natal, Pietermaritzburg Campus, Pietermaritzburg 3201, South Africa.    
  • 2 School of Computer Science, University of KwaZulu-Natal, Pietermaritzburg Campus, Pietermaritzburg 3201, South Africa.    
  • 3 School of Computer Science, University of KwaZulu-Natal, Pietermaritzburg Campus, Pietermaritzburg 3201, South Africa.Unit for Data Science and Computing, North-West University, 11 Hoffman Street, Potchefstroom, 2520, South Africa.    
  • 4 Al-Ahliyya Amman University    
  • 5 Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan    
  • 6 Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, United Arab Emirates    
  • *corresponding author Aligah.2020@gmail.com
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230948
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Detecting a pothole can help prevent damage to your vehicle and potentially prevent an accident. Different techniques, including machine learning, deep learning models, sensor methods, stereo vision, the internet of things (IoT), and black-box cameras, have already been applied to address the problem. However, studies have shown that machine learning and deep learning techniques successfully detect potholes. However, because most of these successful attempts are peculiar to the location of the study, we found no study which has addressed the peculiarity of potholes in South Africa using a tailored-trained deep learning model. In this study, we propose using a convolutional neural network (CNN), a type of deep learning model, to address this growing problem on South African roads. To achieve this, a CNN model was designed from scratch and trained with image samples obtained from the context of the study. The classifier was adapted to distinguish between a binary class which identifies the presence or absence of potholes. Results showed a significant performance enhancement at a classification accuracy of 92.72%. The outcome of this study showed that this machine learning approach holds great potential for addressing the challenge of potholes and road bumps in the region and abroad.

Keywords:

Pothole, Machine Learning, Convolution Neural Network

Bharat,R.;Ikotun,A.M.;Ezugwu,A.E.;Abualigah,L.;Shehab,M.;Zitar,R.A. (2023). A real-time automatic pothole detection system using convolution neural networks. Applied and Computational Engineering,6,750-757.
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References

[1]. Sathishkumar, V. E., Park, J., & Cho, Y. (2020). Using data mining techniques for bike sharing demand prediction in metropolitan city. Computer Communications, 153, 353-366.

[2]. S. Pehere, P. Sanganwar, S. Pawar and A. Shinde. 2020. Detection of pothole by image processing using UAV. Journal of Science and Technology.

[3]. J. Youngtae, R. Seungki. 2015. Pothole Detection System Using a Black-box Camera. Sensors. Basel, Switzerland, Nov. 2015, https://doi.org/10.3390/ s151129316

[4]. X. Yu and E. Salari. 2011. Pavement pothole detection and severity measurement using laser imaging. IEEE International Conference on Electro/Information Technology. Mankato, Minn, USA, May 2011.

[5]. J. Lin and Y. Liu. 2010. Potholes detection based on SVM in the pavement distress image. 9th International Symposium on Distributed Computing and Applications to Business, Engineering, and Science). pp. 544–547, Hong Kong, August 2010.

[6]. D. Avellaneda, J. Lopez-Parra. 2016. Detection and localization of potholes in roadways using smartphones. DYNA 83.

[7]. H. Maeda, Y. Sekimoto, T. Seto, T. Kashiyama, H. Omata. 2018. Road damage detection and classification using deep neural networks with smartphone images. Computer-Aided Civil and Infrastructure Engineering.

[8]. A. Danti, J. Kulkarni and P. Hiremath. 2012. An Image Processing Approach to Detect Lanes, Potholes and Recognize Road Signs in Indian Roads. International Journal of Modeling and Optimization.

[9]. A. A. Shaghouri, R. Alkhatib and S. Berjaoui. 2021. Real-time pothole detection using deep learning. arXiv preprint arXiv:2107.06356.

[10]. Z. Hasan, S. N. Shampa, T. R. Shahidi, S. Siddique. 2020. Pothole and speed breaker detection using smartphone cameras and convolutional neural networks.2020 IEEE Region 10 Symposium (TENSYMP), pp 279–282. https://doi.org/10.1109/TEN SYMP50017.2020.9230587

[11]. Y. B. Aparna, R. Rachna, G. Varun, A. Naveen, A. Aparna. 2019. Convolutional neural networks-based potholes detection using thermal imaging. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.02.004

[12]. O. A. Egaji, G. Evans, M. G. Griffiths, G. Islas. 2021. Real-time machine learning-based approach for pothole detection. Expert Systems with Applications.

[13]. Pothole Detection Dataset, A. Kumar. Retrieved on September 10, 2022, from: https://www.kaggle.com/datasets/atulyakumar98/pothole-detection-dataset

[14]. Pothole 600, Google Sites. Retrieved on September,10, 2022 from: https://sites.google.com/view/pothole-600/dataset

[15]. VE, S., Park, J., & Cho, Y. (2020). Seoul bike trip duration prediction using data mining techniques. IET Intelligent Transport Systems, 14(11), 1465-1474.

[16]. VE, S., & Cho, Y. (2020). A rule-based model for Seoul Bike sharing demand prediction using weather data. European Journal of Remote Sensing, 53(sup1), 166-183.

Cite this article

Bharat,R.;Ikotun,A.M.;Ezugwu,A.E.;Abualigah,L.;Shehab,M.;Zitar,R.A. (2023). A real-time automatic pothole detection system using convolution neural networks. Applied and Computational Engineering,6,750-757.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Sathishkumar, V. E., Park, J., & Cho, Y. (2020). Using data mining techniques for bike sharing demand prediction in metropolitan city. Computer Communications, 153, 353-366.

[2]. S. Pehere, P. Sanganwar, S. Pawar and A. Shinde. 2020. Detection of pothole by image processing using UAV. Journal of Science and Technology.

[3]. J. Youngtae, R. Seungki. 2015. Pothole Detection System Using a Black-box Camera. Sensors. Basel, Switzerland, Nov. 2015, https://doi.org/10.3390/ s151129316

[4]. X. Yu and E. Salari. 2011. Pavement pothole detection and severity measurement using laser imaging. IEEE International Conference on Electro/Information Technology. Mankato, Minn, USA, May 2011.

[5]. J. Lin and Y. Liu. 2010. Potholes detection based on SVM in the pavement distress image. 9th International Symposium on Distributed Computing and Applications to Business, Engineering, and Science). pp. 544–547, Hong Kong, August 2010.

[6]. D. Avellaneda, J. Lopez-Parra. 2016. Detection and localization of potholes in roadways using smartphones. DYNA 83.

[7]. H. Maeda, Y. Sekimoto, T. Seto, T. Kashiyama, H. Omata. 2018. Road damage detection and classification using deep neural networks with smartphone images. Computer-Aided Civil and Infrastructure Engineering.

[8]. A. Danti, J. Kulkarni and P. Hiremath. 2012. An Image Processing Approach to Detect Lanes, Potholes and Recognize Road Signs in Indian Roads. International Journal of Modeling and Optimization.

[9]. A. A. Shaghouri, R. Alkhatib and S. Berjaoui. 2021. Real-time pothole detection using deep learning. arXiv preprint arXiv:2107.06356.

[10]. Z. Hasan, S. N. Shampa, T. R. Shahidi, S. Siddique. 2020. Pothole and speed breaker detection using smartphone cameras and convolutional neural networks.2020 IEEE Region 10 Symposium (TENSYMP), pp 279–282. https://doi.org/10.1109/TEN SYMP50017.2020.9230587

[11]. Y. B. Aparna, R. Rachna, G. Varun, A. Naveen, A. Aparna. 2019. Convolutional neural networks-based potholes detection using thermal imaging. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.02.004

[12]. O. A. Egaji, G. Evans, M. G. Griffiths, G. Islas. 2021. Real-time machine learning-based approach for pothole detection. Expert Systems with Applications.

[13]. Pothole Detection Dataset, A. Kumar. Retrieved on September 10, 2022, from: https://www.kaggle.com/datasets/atulyakumar98/pothole-detection-dataset

[14]. Pothole 600, Google Sites. Retrieved on September,10, 2022 from: https://sites.google.com/view/pothole-600/dataset

[15]. VE, S., Park, J., & Cho, Y. (2020). Seoul bike trip duration prediction using data mining techniques. IET Intelligent Transport Systems, 14(11), 1465-1474.

[16]. VE, S., & Cho, Y. (2020). A rule-based model for Seoul Bike sharing demand prediction using weather data. European Journal of Remote Sensing, 53(sup1), 166-183.

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