Lane and Object Detection using YOLO: Indian Roads Scenario

Chinmay Pimpalgaonkar; Sanket Shinde; Dr. Rahul Sharma

doi:10.55529/jipirs.44.23.29

Lane and Object Detection using YOLO: Indian Roads Scenario

https://doi.org/10.55529/jipirs.44.23.29

Authors

Chinmay Pimpalgaonkar Student at DY PATIL International University, Pune, India.
Sanket Shinde Student at DY PATIL International University, Pune, India.
Dr. Rahul Sharma School of Computer Science Engineering and Applications, DY Patil International University, Pune, India.

Keywords:

Lane, Object Detection, Indian Roads, Technology.

Abstract

This research explores the feasibility of You Only Look Once (YOLO), a deep learning object detection algorithm, for lane and object detection in challenging Indian road environments. Traditional methods struggle with faded lane markings, dense and diverse traffic, and unpredictable scenarios. YOLO's speed and accuracy make it suitable for real-time ADAS applications. The methodology leverages Roboflow, a platform for computer vision tasks, to explore data acquisition, model selection, training, and evaluation. This research aims to contribute to developing safer and more reliable ADAS systems for Indian roads.

Published

2024-07-22

How to Cite

Chinmay Pimpalgaonkar, Sanket Shinde, & Dr. Rahul Sharma. (2024). Lane and Object Detection using YOLO: Indian Roads Scenario . Journal of Image Processing and Intelligent Remote Sensing, 4(4), 23–29. https://doi.org/10.55529/jipirs.44.23.29