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Sheikhzade S, Vahdat-Nejad H, Havangi R. Pothole Detection by Soft Computing. JSDP 2022; 19 (2) : 1
URL: http://jsdp.rcisp.ac.ir/article-1-1101-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1101-en.html
University of Birjand
Abstract: (1157 Views)
Potholes on roads are regarded as serious problems in the transportation domain, and ignoring them lead to an increase in accidents, traffic, vehicle fuel consumption, and waste of time and energy. As a result, pothole detection has attracted researchers’ attention, and different methods have been presented for it up to now. Data analysis methods such as machine learning and soft computing have been widely used for detection purposes. They rely on a dataset and propose a system that can detect a special event in similar datasets. Their effectiveness can be measured by evaluating their accuracy in detecting the event.
Image processing involves a wide range of analytics that are used to extract specific information from images. The majority of image processing programs require massive computational power. The major part of previous research is based on image processing. They utilize dedicated cameras which are embedded in vehicles to take images and analyze them through massive image processing programs. This scheme requires dedicated hardware that is not typically available on vehicles.
In this paper, a new scheme is proposed, which uses accelerometer and GPS sensors. These types of sensors are available in today’s smartphones as well as modern vehicles. The data generated by these sensors is processed via soft computing to increase the accuracy of pothole detection. The proposed algorithm uses a combination of a fuzzy system and evolutionary algorithms. Fuzzy systems have been widely used to model the real-world problems that are described by uncertainty and ambiguity. Evolutionary algorithms (e.g., genetic algorithms) try to imitate evolutionary science in solving hard problems. Genetic algorithm and harmony search are used to adjust membership functions of the proposed fuzzy system.
For evaluation, a case study has been conducted with regard to detect potholes on Ghaffari Street in Birjand. To this end, a real dataset has been collected and used for implementing the proposed method. Experimental results show the high accuracy of the proposed algorithm in comparison to other solutions. They reveal that the accuracy of the proposed genetic fuzzy algorithm is 98 percent and for the proposed harmony fuzzy algorithm is 99 percent.
Image processing involves a wide range of analytics that are used to extract specific information from images. The majority of image processing programs require massive computational power. The major part of previous research is based on image processing. They utilize dedicated cameras which are embedded in vehicles to take images and analyze them through massive image processing programs. This scheme requires dedicated hardware that is not typically available on vehicles.
In this paper, a new scheme is proposed, which uses accelerometer and GPS sensors. These types of sensors are available in today’s smartphones as well as modern vehicles. The data generated by these sensors is processed via soft computing to increase the accuracy of pothole detection. The proposed algorithm uses a combination of a fuzzy system and evolutionary algorithms. Fuzzy systems have been widely used to model the real-world problems that are described by uncertainty and ambiguity. Evolutionary algorithms (e.g., genetic algorithms) try to imitate evolutionary science in solving hard problems. Genetic algorithm and harmony search are used to adjust membership functions of the proposed fuzzy system.
For evaluation, a case study has been conducted with regard to detect potholes on Ghaffari Street in Birjand. To this end, a real dataset has been collected and used for implementing the proposed method. Experimental results show the high accuracy of the proposed algorithm in comparison to other solutions. They reveal that the accuracy of the proposed genetic fuzzy algorithm is 98 percent and for the proposed harmony fuzzy algorithm is 99 percent.
Article number: 1
Type of Study: Research |
Subject:
Paper
Received: 2019/12/19 | Accepted: 2021/12/6 | Published: 2022/09/30 | ePublished: 2022/09/30
Received: 2019/12/19 | Accepted: 2021/12/6 | Published: 2022/09/30 | ePublished: 2022/09/30
References
1. [1] M. Kherad, Traffic assignment of Tehran urban network by Genetic fuzzy algorithm, Master thesis, University of Birjand, 2016.
2. [2] A. Jafari, H. Shahbazi, A review on fuzzy reasoning algorithms, 4th conference of new idea on electrical engineering, 2014.
3. [3] A. Sajjadzadeh, Feeature selection by harmony search algorithm, Master thesis, University of Birjand, 2013.
4. [4] R. Sahraeian, I. Rastgar, "Developing harmony search algorithm for solving optimization problems: a case study in parallel machine production scheduling problem", Journal of Industrial Engineering Research in Production Systems, Vol 1, 2013.
5. [5] G. Alessandroni, LC. Klopfenstein, S. Delpriori, M. Dromedari, G. Luchetti, BD. Paolini, A. Seraghiti, E. Lattanzi, V. Freschi, A. Carini and A. Bogliolo, "SmartRoadSense: collaborative road surface condition monitoring," The Eighth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, UBICOMM, 2014, pp. 210-215.
6. [6] S. Balakuntala and S. Venkatesh, "An intelligent system to detect, avoid and maintain potholes: A graph theoretic approach," arXiv preprint arXiv: 1305.5522, 2013.
7. [7] E. Salari and X. Yu, "Pavement distress detection and classification using a genetic algorithm," IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1-5, 2011. [DOI:10.1109/AIPR.2011.6176378]
8. [8] R. Fan, U. Ozgunalp, I. Pitas "Pothole Detection Based on Disparity Transformation and Road Surface Modeling," IEEE Transactions on Image Processing, pp. 897-908, 2019. [DOI:10.1109/TIP.2019.2933750] [PMID]
9. [9] A. Dhiman, R. Klette, "Pothole Detection Using Computer Vision and Learning," IEEE Transactions on Intelligent Transportation Systems, pp. 3536 - 3550, 2019. [DOI:10.1109/TITS.2019.2931297]
10. [10] X. Ankalikar, T. Bhatia, S. Chowdhary and M. Roja, "Fuzzy controller based pothole detection system," Tania Bhatia et al, Int.J.Computer Technology & Applications, vol. 5, no. 5, pp.1763-1767, 2016.
11. [11] A. Mednis, G. Strazdins, R. Zviedris, G. Kanonirs and L. Selavo, "Real time pothole detection using Android smartphones with accelerometers," International Conference on Distributed Computing in Sensor Systems and Workshops, 2011, pp. 1-6. [DOI:10.1109/DCOSS.2011.5982206]
12. [12] H. Verbruggen and P. Bruijn, "Fuzzy control and conventional control: What is (and can be) the real contribution of fuzzy systems?," Fuzzy Sets and Systems, vol. 90, pp. 151-160, 1997. [DOI:10.1016/S0165-0114(97)00081-X]
13. [13] O. Cordón, "Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases", World Scientific, vol. 19, 2001. [DOI:10.1142/4177]
14. [14] R. Yager, Ronald, and A. Lotfi Zadeh, "An introduction to fuzzy logic applications in intelligent systems", Springer Science & Business Media, vol. 165, 2012.
15. [15] J.-S. R. Jang, C.-T. Sun, and E. Mizutani, "Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence", America: Prentice-Hall, 1997. [DOI:10.1109/TAC.1997.633847]
16. [16] A. P. Engelbrecht, "Computational intelligence: an introduction,", John Wiley & Sons, 3 ed, 2007.
17. [17] K. S. Lee, Z.W. Geem, S. H. Lee and K. W. Bae, "The harmony search heuristic algorithm for discrete structural optimization," Engineering Optimization, vol. 37, no. 7, pp. 663-684, 2005. [DOI:10.1080/03052150500211895]
18. [18] M. Mahdavi, M. Fesanghary and E. Damangir, "An improved harmony search algorithm for solving optimization problems," Applied Mathematics Computation, vol. 188, no. 2, pp. 1567-1579, 2007. [DOI:10.1016/j.amc.2006.11.033]
19. [19] C. Zanchettin and T. B. Ludermir, "A methodology to train and improve artificial neural network's weights and connections," International Joint Conference on IJCNN, 2006, pp. 5267-5274. [DOI:10.1109/IJCNN.2006.247281]
20. [20] Z. W. Geem, Music-Inspired Harmony Search Algorithm," Springer -Verlag Berlin Heidelberg, 2009. [DOI:10.1007/978-3-642-00185-7]
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