Abstract
Potholes are a threat on roads, and their presence compromises driver, vehicle, and pedestrian safety. In developing countries, the primary reason for road accidents is bad road conditions, resulting in human life and property loss. In countries like India, Road maintenance is a challenging activity. Accidents rates are increasing year by year due to the up-surging potholes count. This paper presents the system as "Forward View Guidance for pothole detection for Indian passenger Car." The camera captures video images, and a deep learning algorithm is used to classify the images as potholes and regular roads. The camera will also provide a view of the vehicle's front, highlighting the pothole. Deep learning YOLOv3 and YOLOv5 algorithms are used to train the model and tested with the Kaggle pothole detection datasets to predict the model's accuracy for detection. The proposed system will help monitor the road's condition, count the number of potholes on the road, and generate an alert signal. The performance of the proposed system is evaluated using precision, recall, and average precision (AP). The experimentation results show that the YOLOv5 algorithm performs best than the YOLOv3 algorithm. The precision, recall, and average precision (AP) for YOLOv5 are obtained as 0.763, 0.548, and 0.635, respectively. The system algorithm is implemented on the Raspberry Pi 4B model, which can be easily fitted as an addon system in the vehicle.
Similar content being viewed by others
Data availability
N/A.
Abbreviations
- AI:
-
Artificial Intelligence
- ADAS:
-
Advanced Driver Assistance System
- ML:
-
Machine Learning
- IoT:
-
Internet of Things
- SVM:
-
Support Vector Machine
- KNN:
-
K Nearest Neighbor
- CNN:
-
Convolutional Neural Network
- R-CNN:
-
Region-based Convolutional Neural Network.
- YOLO:
-
You Only Look Once
References
Masihullah, S., Garg, R., Mukherjee, P., Ray, A.: Attention based coupled framework for road and pothole segmentation. In: 25th International Conference on Pattern Recognition (ICPR) (Milan, 2021), pp. 5812–5819. https://doi.org/10.1109/ICPR48806.2021.9412368
Kwang, A., Sung, L., Seung, R., Dongmahn, S.: Detecting a pothole using deep convolutional neural network models for an adaptive shock observing in a vehicle driving. In: 2018 IEEE International Conference on Consumer Electronics (ICCE) (Las Vegas, 2018), pp. 1–2. https://doi.org/10.1109/ICCE.2018.8326142
Lim, S., Kwon, J.: Detection of potholes using a deep convolutional neural network. J Univers Comput Sci. 24(9), 1244–1257 (2018)
Akagic, A., Buza, E., Omanovic, S.: Pothole detection: An efficient vision based method using RGB color space image segmentation. 40th international convention on information and communication technology, electronics and microelectronics (MIPRO) (Opatija, 2017), pp. 1104–1109. https://doi.org/10.23919/MIPRO.2017.7973589
Seung, R., Taehyeong, K., Young-Ro, K.: Image-based pothole detection system for its service and road management system. Math Probl Eng. 2015, 968361, 10 pages (2015). https://doi.org/10.1155/2015/968361
Jin, L., Yayu, L.: Potholes detection based on SVM in the pavement distress image. In: DCABES '10: Proceedings, ninth international symposium on distributed computing and applications to business, engineering and science (2010), pp. 544–547 . https://doi.org/10.1109/DCABES.2010.115
Das, S., Kale, A.: P3De - a novel pothole detection algorithm using 3D depth estimation. In: 2nd International Conference for Emerging Technology (INCET) (Belagavi, 2021), pp. 1–5. https://doi.org/10.1109/INCET51464.2021.9456343
Rastogi, R., Kumar, U., Kashyap, A., Jindal, S., Pahwa, S.: A comparative evaluation of the deep learning algorithms for pothole detection. In: IEEE 17th India Council International Conference (INDICON), vol. 2020 (New Delhi, 2020), pp. 1–6. https://doi.org/10.1109/INDICON49873.2020.9342558
Makeml.app. Available at: https://makeml.app/datasets/potholes and https://www.kaggle.com/datasets/andrewmvd. Database Contents License (DbCL) v1.0. Available online
Fan, R., Ai, X., Dahnoun, N.: Road surface 3D reconstruction based on dense subpixel disparity map estimation. IEEE Trans. Image Process. 27(6), 3025–3035 (2018). https://doi.org/10.1109/TIP.2018.2808770
Ahmed, K.: Smart pothole detection using deep learning based on dilated convolution. Sensors. 21(24), 8406 (2021)
Funding
N/A.
Author information
Authors and Affiliations
Contributions
SC carried out literature review, contributed in data collection, analysis and interpretation of results, and was major contributor in manuscript preparation. AB helped in hardware implementation and final drafting of manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics Approval
No approval required. Manuscript doesn’t include any data related human participants, human data and human tissue.
Consent for Publication
I, the undersigned, give my consent for the publication of identifiable details, which can include photograph(s) and/or videos and/or case history and/or details within the text (“Material”) to be published in the above Journal and Article.
Competing Interest
No conflict of interest
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chougule, S., Barhatte, A. Smart Pothole Detection System using Deep Learning Algorithms. Int. J. ITS Res. 21, 483–492 (2023). https://doi.org/10.1007/s13177-023-00363-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13177-023-00363-3