• Mrs.Sathya M
• Jayaprakash B
• BARATH KUMAR R M
• DINESH S

Road infrastructure is essential for safe and efficient transportation, but potholes can create significant risks for vehicles and pedestrians, leading to accidents and higher maintenance costs. This paper presents a novel approach to pothole detection using AI and machine learning techniques. The system processes uploaded images or videos of roads and employs d eep learning models to detect potholes based on visual features such as depth, shape, and surface texture. The model is trained on a diverse dataset of road conditions, ensuring high accuracy and adaptability. By automating the detection process through image and video analysis, this approach enhances road maintenance efficiency and reduces the reliance on manual inspections. The effectiveness of the proposed system is validated through performance tests, demonstrating improved detection rates compared to traditional methods. This research contributes to smarter urban planning and helps ensure proactive road maintenance, ultimately improving safety and reducing costs for municipalities.

Pothole Detection, Computer Vision, Machine Learning, Image Processing, Deep Learning, Road Infrastructure, Uploaded Image/Video Analysis, Surface Feature Detection, Automated Maintenance, Dataset Annotation, Image Recognition, Road Safety, Urban Planning, Transportation Efficiency, Proactive Maintenance Solutions.

"Road pothole detection using AI and Computer vision", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.b299-b301, November-2024, Available :http://www.jetir.org/papers/JETIR2411135.pdf

"Road pothole detection using AI and Computer vision", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppb299-b301, November-2024, Available at : http://www.jetir.org/papers/JETIR2411135.pdf