Pothole Detection Using Machine Learning Models
DOI:
https://doi.org/10.32628/IJSRSET241126Keywords:
YOLO, CNN, SSD, HOG, Potholes, Data, Machine Learning, Regression, Dilation, Erosion, ClosingAbstract
Potholes are damage caused to the ground by the formation of water and wear and tear over time. According to statistical data, bad road conditions account for about one- third of the total road accidents which has been increasing exponentially. Potholes have become so common that it has become second nature for people to learn how to spot and avoid them, which causes further accidents. The need of the hour is to build a dependable pothole detection system to accurately detect potholes and warn the drivers and government officials in advance. The process to build such a system is divided into two steps i.e. collection of data and pothole identification. The first step is achieved by taking the data from already available data sets on the Internet. The other step includes labeling the potholes in the data set which is usually done manually. This paper focuses mainly on Visual-based techniques to identify the best detection method by comparing popular Machine Learning models and algorithms. The obtained data set is trained using various transfer learning techniques like You Only Look Once (YOLO)[1] and Single Shot Detector (SSD) [1]. Apart from transfer learning, this paper also focuses on some proposed techniques using Convolutional Neural Net- works (CNN) and classification algorithms like Support Vector Machine (SVM)[21] to identify and localize potholes. The actual size of potholes is calculated using morphological operations, which is a just a straightforward technique to analyze figures using set theory. To analyze every model and find the best model, each model is trained on different sizes of data sets and the obtained result is validated and examined by considering different aspects like speed and accuracy in mind.
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References
Anon, (2019). [online] Available at: https://www.pothole.info/the- facts/ [Accessed 13 Mar. 2019].
J. Lin, Y. Liu, ”Potholes detection based on SVM in the pavement distress image”, Appl. Bus. Eng. Sci, pp. 544-547, Aug. 2010. DOI: https://doi.org/10.1109/DCABES.2010.115
YoungJin Cha, Wooram Choi, Oral Bykztrk, ”Deep LearningBased Crack Damage Detection Using Convolutional Neural Networks”, 2017.
Hiroya Maeda, Yoshihide Sekimoto, Toshikazu Seto, Takehiro Kashiyama, Hiroshi Omata, Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone, 4- 6-1 Komaba, Tokyo, Japan:University of Tokyo.
Justin Bray, Brijesh Verma, Xue Li, Wade He, ”A Neural Network based Technique for Automatic Classification of Road Cracks”, 2006 International Joint Conference on Neural Networks Sheraton Vancou- ver Wall Centre Hotel, July 16-21, 2006. DOI: https://doi.org/10.1109/IJCNN.2006.246782
Allen Zhang, Kelvin C. P. Wang, Baoxian Li, Enhui Yang, Xianxing Dai, Yi Peng, Yue Fei, Yang Liu, Joshua Q. Li, Cheng Chen, ”Automated Pixel - Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network”, Computer-Aided Civil and Infrastructure Engineering, vol. 00, pp. 1-15, 2017. DOI: https://doi.org/10.1111/mice.12297
Lei Zhang, Fan Yang, Yimin Daniel Zhang, Ying Julie Zhu, ”Road Crack Detection Using Deep Convolutional Neural Network”.
A. Tedeschi, F. Benedetto, ”A real-time automatic pavement crack and pothole recognition system for mobile Android-based devices”, Advanced Engineering Informatics, vol. 32, pp. 11-25, 2017. DOI: https://doi.org/10.1016/j.aei.2016.12.004
A. Sachdeva and A. Sachdeva, “YOLO - ’You only look once’ for Object Detection explained,” Medium, 26-Mar-2017. [Online]. Available: https://medium.com/diaryofawannapreneur/yolo-you-only- look-once-for-object-detection-explained-6f80ea7aaa1e. [Accessed: 13-Mar-2019].
M. Hollemans, Real-time object detection with YOLO. [On- line]. Available: http://machinethink.net/blog/object-detection-with- HYPERLINK "http://machinethink.net/blog/object-detection-with-%20yolo/" HYPERLINK "http://machinethink.net/blog/object-detection-with-%20yolo/"yolo/. [Accessed: 13-Mar-2019].
Liu, Wei, Anguelov, Dragomir, Erhan, Dumitru, Scott, Cheng- Yang, Berg, A. C., and Reed, “SSD: Single Shot MultiBox Detector,” arXiv.org, 29-Dec-2016. [Online]. Available: https://arxiv.org/abs/1512.02325. [Accessed: 13-Mar-2019].
Byeong-ho Kang , Su-il Choi, “Pothole detection system using 2D LiDAR and camera”, 2017 Ninth International Conference on Ubiq- uitous and Future Networks (ICUFN)
Sudarshan S. Rode , Shonil Vijay , Prakhar Goyal, Purushottam Kulkarni, Kavi Arya, “Pothole Detection and Warning System: Infras- tructure Support and System Design”, 2009 International Conference on Electronic Computer Technology
X. Yu, E. Salari, “Pavement pothole detection and severity measure- ment using laser imaging”, 2011 IEEE INTERNATIONAL CONFER- ENCE ON ELECTRO/INFORMATION TECHNOLOGY DOI: https://doi.org/10.1109/EIT.2011.5978573
Yaqi Li, Christos Papachristou, Daniel Weyer, “Road Pothole Detec- tion System Based on Stereo Vision”, NAECON 2018 - IEEE National Aerospace and Electronics Conference
Alfandino Rasyid, Mochammad Rifki Ulil Albaab, Muhammad Fajrul Falah, Yohanes Yohanie Fridelin Panduman, Alviansyah Arman Yusuf, Dwi Kurnia Basuki, Anang Tjahjono, Rizqi Putri Nourma Budiarti, Sritrusta Sukaridhoto, Firman Yudianto, Hendro Wicaksono, “Pothole Visual Detection using Machine Learning Method integrated with Internet of Thing Video Streaming Platform”, IEEE, 2017
Vinay Rishiwal, Hamshan Khan, “Automatic pothole and speed breaker detection using android system”,2016 39th International Con- vention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) DOI: https://doi.org/10.1109/MIPRO.2016.7522334
Ya-Wen Hsu, Jau-Woei Perng, Zong-Han Wu, “Design and imple- mentation of an intelligent road detection system with multisensor integration
Sandeep Venkatesh, E. Abhiram, S. Rajarajeswari, K. M. Sunil Kumar, Shreyas Balakuntala, Nitin Jagadish, “An intelligent system to detect, avoid and maintain potholes: A graph theoretic approach” 2014 Seventh International Conference on Mobile Computing and Ubiquitous Networking (ICMU) DOI: https://doi.org/10.1109/ICMU.2014.6799066
Sumit Srivastava, Ayush Sharma, Harsh Balot, “Analysis and Improve- ments on Current Pothole Detection Techniques” 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) DOI: https://doi.org/10.1109/ICSCEE.2018.8538390
Amita Dhiman, Reinhard Klette, “Pothole Detection Using Computer Vision and Learning”,IEEE Transactions on Intelligent Transportation Systems, 2019 DOI: https://doi.org/10.1109/TITS.2019.2931297
Andrew Fox ; B.V.K. Vijaya Kumar ; Jinzhu Chen ; Fan Bai, “Multi- Lane Pothole Detection from Crowdsourced Undersampled Vehicle Sensor Data” Year: 2017 — Volume: 16, Issue: 12 — Journal Article Publisher: IEEE DOI: https://doi.org/10.1109/TMC.2017.2690995
Ricardo Silveira Rodrigues, Marcia Pasin, Alice Kozakevicius, Vini- cius Monego, “Pothole Detection in Asphalt: An Automated Approach to Threshold Computation Based on the Haar Wavelet Transform”, 2019 IEEE 43rd Annual Computer Software and Applications Con- ference (COMPSAC) DOI: https://doi.org/10.1109/COMPSAC.2019.00053
Mae M. Garcillanosa, Jian Mikee L. Pacheco, Rowie E. Reyes, Junelle Joy P. San Juan, “Smart Detection and Reporting of Potholes via Image-Processing using Raspberry-Pi Microcontroller”, Year: 2018 — Conference Paper — Publisher: IEEE DOI: https://doi.org/10.1109/KST.2018.8426203
Tran Duc Chung, M. K. A. Ahamed Khan, “Watershed-based Real- time Image Processing for Multi-Potholes Detection on Asphalt Road” 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET) DOI: https://doi.org/10.1109/ICSEngT.2019.8906371
Sung Won Lee ; SeokJin Kim, Jeong Han, Kwang Eun An, Seung-Ki Ryu , Dongmahn Seo, “Experiment of Image Processing Algorithm for Efficient Pothole Detection” 2019 IEEE International Conference on Consumer Electronics (ICCE)
Rui Fan ; Umar Ozgunalp ; Brett Hosking ; Ming Liu ; Ioannis Pitas “Pothole Detection Based on Disparity Transformation and Road Surface Modeling ”,IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium DOI: https://doi.org/10.1109/TIP.2019.2933750
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Copyright (c) 2024 Mayank Dhingra, Rahul Dhingra, Meghna Sharma (Author)
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