Pothole Detection and dimension Estimation using deep learning (YOLO) and image processing.
Abstract & Details
Research Area
Computer Engineering
Keywords
Deep Learning
Pothole Detection
Convolutional neural networks(CNNs)
YOLO(You Only Look Once)
Open CV
Pandas
Numpy
Scikit
Tensor Flow
Keras
Pytorch
Abstract
The world is advancing towards an autonomous environment at a great pace and it has become a need of an hour, especially during the current pandemic situation. The pandemic has hindered the functioning of many sectors, one of them being Road development and maintenance. Creating a safe working environment for workers is a major concern of road maintenance during such difficult times. This can be achieved to some extent with the help of an autonomous system that will aim at reducing human dependency. In this paper, one of such systems, a pothole detection and dimension estimation, is proposed. The proposed system uses a Deep Learning based algorithm YOLO (You Only Look Once) for pothole detection. Further, an image processing based triangular similarity measure is used for pothole dimension estimation. The proposed system provides reasonably accurate results of both pothole detection and dimension estimation. The proposed system also helps in reducing the time required for road maintenance. The system uses a custom made dataset consisting of images of water-logged and dry potholes of various shapes and sizes.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Bhargav Ram Gowrisetty | Vasireddy Venkatadri Institute of Technology |
| 2 | Hareesh Reddy Emani | Vasireddy Venkatadri Institute of Technology |
| 3 | Vineeth Kumar Ganta | Vasireddy Venkatadri Institute of Technology |
| 4 | Doushik Chinthapudi | Vasireddy Venkatadri Institute of Technology |
| 5 | Dr. Shaik. Riyazuddien | Vasireddy Venkatadri Institute of Technology |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Gowrisetty, Bhargav Ram, Emani, Hareesh Reddy, Ganta, Vineeth Kumar, Chinthapudi, Doushik, & Riyazuddien, Dr. Shaik. (2024). Pothole Detection and dimension Estimation using deep learning (YOLO) and image processing.. International Journal of Advance Research and Innovative Ideas In Education, 10(2), 748-754.
MLA Style
Gowrisetty, Bhargav Ram, et al. "Pothole Detection and dimension Estimation using deep learning (YOLO) and image processing.." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 2, 2024, pp. 748-754.
IEEE Style
Bhargav Ram Gowrisetty, Hareesh Reddy Emani, Vineeth Kumar Ganta, Doushik Chinthapudi, and Dr. Shaik. Riyazuddien, "Pothole Detection and dimension Estimation using deep learning (YOLO) and image processing.," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 2, pp. 748-754, 2024.
Vancouver Style
Gowrisetty Bhargav Ram, Emani Hareesh Reddy, Ganta Vineeth Kumar, Chinthapudi Doushik, Riyazuddien Dr. Shaik.. Pothole Detection and dimension Estimation using deep learning (YOLO) and image processing.. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(2):748-754.
Harvard Style
Gowrisetty, Bhargav Ram, Emani, Hareesh Reddy, Ganta, Vineeth Kumar, Chinthapudi, Doushik, & Riyazuddien, Dr. Shaik. (2024) 'Pothole Detection and dimension Estimation using deep learning (YOLO) and image processing.', International Journal of Advance Research and Innovative Ideas In Education, 10(2), pp. 748-754.
Chicago Style
Gowrisetty, Bhargav Ram, et al. "Pothole Detection and dimension Estimation using deep learning (YOLO) and image processing.." International Journal of Advance Research and Innovative Ideas In Education 10, no. 2 (2024): 748-754.
Turabian Style
Gowrisetty, Bhargav Ram, et al. "Pothole Detection and dimension Estimation using deep learning (YOLO) and image processing.." International Journal of Advance Research and Innovative Ideas In Education 10, no. 2 (2024): 748-754.
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