ROAD POTHOLE DETECTION USING AI AND COMPUTER VISION

Abstract

Maintaining road infrastructure is essential for ensuring public safety and vehicle longevity. Potholes, which are depressions in the road surface caused by wear and tear, pose significant hazards and can lead to accidents and vehicle damage. Traditional pothole detection methods rely on manual inspection, which is time-consuming, labor-intensive, and prone to errors. This project aims to develop an automated pothole detection system using computer vision and deep learning techniques. By leveraging OpenCV fo r image processing and Tensor Flow Keras or PyTorch for deep learning model development, the system w ill provide accurate and timely detection o f potholes, thereby enhancing road maintenance efficiency. The system utilizes image enhancement techniques such as noise reduction, edge detection, and thresholding to preprocess road images and videos. Deep learning models trained on a diverse dataset o f annotated road images and videos w ill id e n tify potholes with high accuracy. Labelling w ill be used for dataset annotation, ensuring precise labeling o f potholes. The system will analyze the images and videos to detect potholes, and visualizations using M atplotli will provide clear results for road maintenance teams. Detected potholes w ill be highlighted, and their locations can be documented for prompt and targeted repair actions. This automated detection system w ill reduce the need for manual inspections, accelerate road repairs, and contribute to safer road infrastructure.

Specification

The field o f invention for this project is Automated Road Maintenance and Safety.
Specifically, it involves the application o f Artificial Intelligence (A l), Computer Vision, and Deep Learning for Automated Pothole Detection and Road Renovation. This interdisciplinary field combines elements o f Civil Engineering, Transportation Engineering, Computer Science, and Machine Learning to develop innovative solutions for identifying and addressing road surface defects. The project aims to
Ati significantly enhance road .safety, reduce vehicle damgapdjrpprp^the.pfcienGy f road maintenance and renovation operations b'ym funicipakcorp"bra t'i ons't hroti glfa d vane ed te hibIogical methods.

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