• Snehal Kavathekar
• Ashwini Jarali
• Sanskruti Lad
• Prajwal Lalpotu
• Shreya Jadhav

This paper reviews recent advancements in pothole detection technologies, comparing various methods including deep learning models like YOLO and SSD, and UAV-based systems with multispectral imaging. YOLO v8 Nano emerges as a highly effective model, balancing speed and accuracy in real-time detection, while SSD demonstrates superior precision in certain scenarios. Additionally, UAVs enhance detection by providing early insights into asphalt damage. Image processing techniques and manually labelled datasets are also employed to improve model training and accuracy. The paper evaluates the strengths and limitations of these methods, examining factors like computational efficiency, environmental adaptability, and realtime application. It further explores future directions in this field, focusing on optimizing detection techniques and integrating advanced sensors to enhance road safety and maintenance.

YOLO(You Look Only Once), Pothole Detection

"A Review of Pothole Detection Using Different Technologies ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.h11-h17, December-2024, Available :http://www.jetir.org/papers/JETIR2412702.pdf

"A Review of Pothole Detection Using Different Technologies ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. pph11-h17, December-2024, Available at : http://www.jetir.org/papers/JETIR2412702.pdf