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Title: :  Real-Time Pothole Detection: A YOLO-Based Approach for Safer Roads
PaperId: :  23070
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-23070
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Nithin RBannari Amman Institute of Technology
Shailesh PBannari Amman Institute of Technology
Mohammed Aswath SBannari Amman Institute of Technology

Abstract

Computer Science and Engineering
Deep Learning, Potholes, Road Safety,YOLO Algorithm, Object Detection, Convolutional Neural works, Real-time Detection, Yolo Version 8
Potholes are a major hazard on highways since they are the primary cause of accidents. Preventing accidents requires early detection and correction. These traffic dangers are a difficulty for drivers, especially during the rainy season. To solve this problem, a variety of methods have been used, including vibration-based sensors and manual examination. Fixing potholes is an essential part of maintaining roads, and regulating organizations always struggle to keep an eye on the state of the pavement. An easier way to detect potholes is required because the current approach requires tedious manual picture processing. But there are disadvantages to the current approaches, namely high prices and dangers associated with detection. A non-invasive method using the YOLO (You Only Look Once) algorithm has been proposed to get around these restrictions. Convolutional neural networks (CNNs) are used by YOLO, a real-time object identification system, to identify and classify potholes in photographs. This deep learning project also tries to address pothole issues encountered by self-driving or autonomous automobiles. Real-time pothole identification is provided by this method using emphasized visual clues. By simultaneously predicting bounding boxes and object classes, the CNN-based system improves accuracy and responsiveness. For training, testing, and validation purposes, a dataset including 720x720 pixel quality photos that capture various pothole scenarios in natural road conditions was employed. Using CNN-based object identification algorithms, this unique approach seeks to enable real-time pothole detection and highlighting. This study offers a thorough assessment of YOLOv8, an object identification model, with a focus on identifying potholes and other road hazards.

Citations

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IJARIIE Nithin R, Shailesh P, and Mohammed Aswath S. "Real-Time Pothole Detection: A YOLO-Based Approach for Safer Roads" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 2411-2415
MLA Nithin R, Shailesh P, and Mohammed Aswath S. "Real-Time Pothole Detection: A YOLO-Based Approach for Safer Roads." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 2411-2415.
APA Nithin R, Shailesh P, & Mohammed Aswath S. (2024). Real-Time Pothole Detection: A YOLO-Based Approach for Safer Roads. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 2411-2415.
Chicago Nithin R, Shailesh P, and Mohammed Aswath S. "Real-Time Pothole Detection: A YOLO-Based Approach for Safer Roads." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 2411-2415.
Oxford Nithin R, Shailesh P, and Mohammed Aswath S. 'Real-Time Pothole Detection: A YOLO-Based Approach for Safer Roads', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 2411-2415. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Real_Time_Pothole_Detection__A_YOLO_Based_Approach_for_Safer_Roads_ijariie23070.pdf (Accessed : ).
Harvard Nithin R, Shailesh P, and Mohammed Aswath S. (2024) 'Real-Time Pothole Detection: A YOLO-Based Approach for Safer Roads', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 2411-2415IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Real_Time_Pothole_Detection__A_YOLO_Based_Approach_for_Safer_Roads_ijariie23070.pdf (Accessed : )
IEEE Nithin R, Shailesh P, and Mohammed Aswath S, "Real-Time Pothole Detection: A YOLO-Based Approach for Safer Roads," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 2411-2415, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Real_Time_Pothole_Detection__A_YOLO_Based_Approach_for_Safer_Roads_ijariie23070.pdf [Accessed : ].
Turabian Nithin R, Shailesh P, and Mohammed Aswath S. "Real-Time Pothole Detection: A YOLO-Based Approach for Safer Roads." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 ().
Vancouver Nithin R, Shailesh P, and Mohammed Aswath S. Real-Time Pothole Detection: A YOLO-Based Approach for Safer Roads. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(2) : 2411-2415. Available from: https://ijariie.com/AdminUploadPdf/Real_Time_Pothole_Detection__A_YOLO_Based_Approach_for_Safer_Roads_ijariie23070.pdf
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