Authors : Anushka Kadam; Anushka Patil; Aditya Pore

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/yyw8fvsa

Scribd : https://tinyurl.com/3sf7cmb2

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV509

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The construction and development of good roads is important for the economy and prosperity of today's countries. Road maintenance faced many challenges due to heavy traffic, insufficient funds, and lack of resources. These roads and highways are very dangerous for drivers due to damage such as potholes. Bad roads can lead to dangerous problems such as damaged wheels, tires, damaged vehicles, serious accidents, irregular traffic, and poor driving. To better solve this problem, this project presents a deep learning method that can identify and classify pots according to their size and severity. Drones, also known as unmanned aerial vehicles (UAVs), can be utilized to modernize road safety practices and so enhance the nation's infrastructure. The image library is employed for the development of a pothole detection model, which is subsequently utilized within an algorithmic framework that integrates a road color model with fundamental image processing techniques, including a Canny filter and contour detection. This approach enhances the level of accuracy in identify ing potholes.

Keywords : Pothole, Unmanned Aerial Vehicles (Uavs), YOLO V8, Geotagging and Geomapping.

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