Year: 2024
Type: article
Abstract: This paper aims to develop an automated system for pothole and lane marking detection on roads using the YOLOv4- tiny model with a supporting database. The traditional methods of detecting road conditions are slow and inefficient, as they are based on manual processing. With computer vision, our system will identify potholes and lanes much more quickly and accurately. The YOLOv4-tiny model is chosen because it provides a good balance between speed and accuracy, making it suitable for real-time applications. The database of images of roads with potholes and lanes will support training and testing of the models to achieve reliable detection. It is anticipated that this paper will be helpful in the improvement of road safety and maintenance as it will be able to provide timely information about road conditions.
Author: Supriya Agre
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404081649