Deep CNN and twin support vector machine based model for detecting potholes in road network
References
- Buza, E., Omanovic, S., & Huseinovic, A. (2013, October). Pothole Detection with Image Processing and Spectral Clustering. In proceedings of the 2nd International Conference on Information Technology and Computer Networks (Vol. 810, P. 4853).
- Cao, M., Tran, Q., Nguyen, N., & Chang, K. (2020). Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources. Advanced Engineering Informatics, 46, 101182. DOI: 10.1016/j.aei.2020.101182
- Chougule, S., & Barhatte, A. (2023). Smart Pothole Detection System using Deep Learning Algorithms. International Journal of Intelligent Transportation Systems Research, 21(3), 483–492. DOI: 10.1007/s13177-023-00363-3
- Deepak, S., & Ameer, P. (2022b). Brain tumor categorization from imbalanced MRI dataset using weighted loss and deep feature fusion. Neurocomputing, 520, 94–102. DOI: 10.1016/j.neucom.2022.11.039
- Dhiman, A., & Klette, R. (2019). Pothole detection using computer vision and learning. IEEE Transactions on Intelligent Transportation Systems, 21(8), 3536–3550. DOI: 10.1109/tits.2019.2931297
- Du, R., Qiu, G., Gao, K., Hu, L., & Liu, L. (2020). Abnormal road surface recognition based on smartphone acceleration sensor. Sensors, 20(2), 451. DOI: 10.3390/s20020451
- Eriksson, J., Girod, L., Hull, B., Newton, R., Madden, S., & Balakrishnan, H. (2008). The pothole patrol. DOI: 10.1145/1378600.1378605
- Fan, R., Ai, X., & Dahnoun, N. (2018). Road surface 3D reconstruction based on dense subpixel disparity map estimation. IEEE Transactions on Image Processing, 27(6), 3025–3035. DOI: 10.1109/tip.2018.2808770
- Gagliardi, V., Giammorcaro, B., Francesco, B., & Sansonetti, G. (2023). Deep neural networks for asphalt pavement distress detection and condition assessment. In Earth Resources and Environmental Remote Sensing/GIS Applications XIV, 35. DOI: 10.1117/12.2688512
- Gopalakrishnan, K., Khaitan, S. K., Choudhary, A., & Agrawal, A. (2017). Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction and Building Materials, 157, 322–330. DOI: 10.1016/j.conbuildmat.2017.09.110
- Heo, D., Choi, J., Kim, S., Tak, T., & Zhang, S. (2023). Image-Based pothole detection using Multi-Scale feature network and risk assessment. Electronics, 12(4), 826. DOI: 10.3390/electronics12040826
- Hoang, N., & Nguyen, Q. (2018). A novel method for asphalt pavement crack classification based on image processing and machine learning. Engineering With Computers, 35(2), 487–498. DOI: 10.1007/s00366-018-0611-9
- Khater, H. A., & Gamel, S. A. (2023). Early diagnosis of respiratory system diseases (RSD) using deep convolutional neural networks. Journal of Ambient Intelligence and Humanized Computing, 14(9), 12273–12283. DOI: 10.1007/s12652-023-04659-w
- Koch, C., & Brilakis, I. (2011). Pothole detection in asphalt pavement images. Advanced Engineering Informatics, 25(3), 507–515. DOI: 10.1016/j.aei.2011.01.002
- Laurent, J., Hébert, J. F., Lefebvre, D., & Savard, Y. (2012). Using 3D laser profiling sensors for the automated measurement of road surface conditions. In Rilem bookseries (pp. 157–167). DOI: 10.1007/978-94-007-4566-7_16
- Lee, S., Le, T. H. M., & Kim, Y. (2022). Prediction and detection of potholes in urban roads: Machine learning and deep learning based image segmentation approaches. Developments in the Built Environment, 13, 100109. DOI: 10.1016/j.dibe.2022.100109
- Li, S., Yuan, C., Liu, D., & Cai, H. (2016). Integrated processing of image and GPR data for automated pothole detection. Journal of Computing in Civil Engineering, 30(6). DOI: 10.1061/(asce)cp.1943-5487.0000582
- Moosaei, H., Ganaie, M., Hladík, M., & Tanveer, M. (2022). Inverse free reduced universum twin support vector machine for imbalanced data classification. Neural Networks, 157, 125–135. DOI: 10.1016/j.neunet.2022.10.003
- Nogales, R. E., & Benalcázar, M. E. (2023). Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory. Big Data and Cognitive Computing, 7(2), 102. DOI: 10.3390/bdcc7020102
- Pan, Y., Zhang, X., Tian, J., Jin, X., Luo, L., & Yang, K. (2017). Mapping asphalt pavement aging and condition using multiple endmember spectral mixture analysis in Beijing, China. Journal of Applied Remote Sensing, 11(1), 016003. DOI: 10.1117/1.jrs.11.016003
- Peralta-López, J., Morales-Viscaya, J., Lázaro-Mata, D., Villaseñor-Aguilar, M., Prado-Olivarez, J., Pérez-Pinal, F., Padilla-Medina, J., Martínez-Nolasco, J., & Barranco-Gutiérrez, A. (2023). Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera. Applied Sciences, 13(14), 8349. DOI: 10.3390/app13148349
- Pereira, V., Tamura, S., Hayamizu, S., & Fukai, H. (2018). Pereira, V., Tamura, S., Hayamizu, S., & Fukai, H. (2018, July). 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. In IEEE International Conference on Service Operations and Logistics, and Informatics. DOI: 10.1109/soli.2018.8476795
- Satti, S. K., Rajareddy, G. N. V., Mishra, K., & Gandomi, A. H. (2024). Potholes and traffic signs detection by classifier with vision transformers. Scientific Reports, 14(1). DOI: 10.1038/s41598-024-52426-4
- Singh, P., Bansal, A., & Kumar, S. (2020). Performance Analysis of various Information Platforms for recognizing the quality of Indian Roads. 2022 12th International Conference on Cloud Computing, Data Science &Amp; Engineering (Confluence), 63–76. DOI: 10.1109/confluence47617.2020.9057829
- Singh, P., Kamal, A. E., Bansal, A., & Kumar, S. (2023). Road pothole detection from smartphone sensor data using improved LSTM. Multimedia Tools and Applications, 83(9), 26009–26030. DOI: 10.1007/s11042-023-16177-0
- Tahir, H., & Jung, E. (2023). Comparative study on distributed lightweight deep learning models for road pothole detection. Sensors, 23(9), 4347. DOI: 10.3390/s23094347
- Tamagusko, T., & Ferreira, A. (2023). Optimizing Pothole Detection in Pavements: A Comparative Analysis of Deep Learning Models. Optimizing Pothole Detection in Pavements: A Comparative Analysis of Deep Learning Models, 57, 11. DOI: 10.3390/engproc2023036011
- Tsai, Y., & Chatterjee, A. (2017). Pothole detection and classification using 3D technology and watershed method. Journal of Computing in Civil Engineering, 32(2). DOI: 10.1061/(asce)cp.1943-5487.0000726
- Vinodhini, K. A., & Sidhaarth, K. R. A. (2023). Pothole detection in bituminous road using CNN with transfer learning. Measurement Sensors, 31, 100940. DOI: 10.1016/j.measen.2023.100940
- Wang, H., Chen, C., Cheng, D., Lin, C., & Lo, C. (2015). A Real-Time pothole detection approach for intelligent transportation system. Mathematical Problems in Engineering, 2015, 1–7. DOI: 10.1155/2015/869627
- Wang, H., Zhu, J., & Feng, F. (2023). Elastic net twin support vector machine and its safe screening rules. Information Sciences, 635, 99–125. DOI: 10.1016/j.ins.2023.03.131
- Wu, C., Wang, Z., Hu, S., Lepine, J., Na, X., Ainalis, D., & Stettler, M. (2020). An Automated Machine-Learning approach for road pothole detection using smartphone sensor data. Sensors, 20(19), 5564. DOI: 10.3390/s20195564
- Xin, H., Ye, Y., Na, X., Hu, H., Wang, G., Wu, C., & Hu, S. (2023). Sustainable road pothole Detection: a crowdsourcing based Multi-Sensors fusion approach. Sustainability, 15(8), 6610. DOI: 10.3390/su15086610
- Xu, Y., Sun, T., Ding, S., Yu, J., Kong, X., Ni, J., & Shi, S. (2023). VIDAR-Based Road-Surface-Pothole-Detection Method. Sensors, 23(17), 7468. DOI: 10.3390/s23177468
- Xue, G., Zhu, H., Hu, Z., Yu, J., Zhu, Y., & Luo, Y. (2016). Pothole in the Dark: Perceiving Pothole Profiles with Participatory Urban Vehicles. IEEE Transactions on Mobile Computing, 16(5), 1408–1419. DOI: 10.1109/tmc.2016.2597839
- Yu, G., Ma, J., & Xie, C. (2022). Hessian scatter regularized twin support vector machine for semi-supervised classification. Engineering Applications of Artificial Intelligence, 119, 105751. DOI: 10.1016/j.engappai.2022.105751
- Zalama, E., Gómez-García-Bermejo, J., Medina, R., & Llamas, J. (2013). Road crack detection using visual features extracted by Gabor filters. Computer-Aided Civil and Infrastructure Engineering, 29(5), 342–358. DOI: 10.1111/mice.12042
- Zhang, A., Wang, K. C. P., & Ai, C. (2017). 3D shadow modeling for detection of descended patterns on 3D pavement surface. Journal of Computing in Civil Engineering, 31(4). DOI: 10.1061/(asce)cp.1943-5487.0000661
- Zhang, L., Yang, F., Zhang, Y. D., & Zhu, Y. J. (2016). Road crack detection using deep convolutional neural network. 2022 IEEE International Conference on Image Processing (ICIP), 3708–3712. DOI: 10.1109/icip.2016.7533052
- Zhao, M., Su, Y., Wang, J., Liu, X., Wang, K., Liu, Z., Liu, M., & Guo, Z. (2024). MED-YOLOv8s: a new real-time road crack, pothole, and patch detection model. Journal of Real-Time Image Processing, 21(2). DOI: 10.1007/s11554-023-01405-5
DOI: https://doi.org/10.2478/ijssis-2025-0099 | Journal eISSN: 1178-5608
Language: English
Submitted on: Apr 14, 2025
Published on: Aug 11, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
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© 2025 Mohit Misra, Saptarshi Gupta, Shailesh Tiwari, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.