Road Sense: Intelligent Road Monitoring System

Abstract

This project proposes a system for detecting traffic signals, lane layouts, and speed bumps in road infrastructure using video footage through machine learning. Lane detection is performed through region of interest selection and edge detection. Lane lines are extracted based on specific characteristics. A deep learning model is trained to detect lane boundaries and road curvature. The system provides real-time alerts and recommendations to enhance road safety and driving experiences.

Introduction

I. INTRODUCTION

Our advanced system combines computer vision and machine learning to enhance road. Safety and traffic management. By accurately detecting traffic signals, lane layouts, and curves, speed bumps in real-time, our solution aims to improve driver awareness and optimize traffic flow.

Our objective is to develop a robust real-time system for identifying and classifying various traffic signals. Through image preprocessing and color segmentation, we isolate signal regions based on their distinct colors.

Advanced algorithms, including blob detection, distinguish signals from other objects. We extract essential features like shape, size, position, and color to characterize the traffic signals.

Machine learning techniques, such as pattern recognition and deep learning, classify these features accurately, determining the specific signal type.

In addition, our system includes advanced lane detection. By defining a region of interest (ROI) and utilizing Canny edge detection, we identify lane markings with high contrast against the road surface. This enables us to extract lane lines for improved driver assistance and lane-keeping functionality.

II. PROPOSED METHODOLOGY

???????A. Image Extraction and Preprocessing

The project proposes a system for detection of traffic and road signals, lane and curve layouts for roads. It involves image or frames acquisition from video footage captured using cameras. This is followed by the image preprocessing to enhance resolution of the image and reduce noise.

???????B. Feature Extraction and Segmentation

Color segmentation is done to isolate regions containing the colors of the traffic signals by setting appropriate threshold values for hue, saturation, and value (HSV). Relevant features are then extracted from blobs to characterize the traffic signal. These features may include shape, size, position, and color information. A machine learning model is employed to classify the extracted features and determine the type of traffic signal. This is done using deep learning- based approaches of YOLO v8.

For lane detection, region of interest is defined to focus only on the road area. Typically, this region is a trapezoidal shape representing the area in front of the vehicle where lanes are expected to be present. Canny edge detection is applied to detect edges within the ROI. This helps identify lane markings that exhibit significant contrast with the road surface.

For speed bump detection, filtering the detected objects to focus on potential speed bumps based on their size, shape, and location within the frame. We use a classifier to distinguish speed bumps from other objects in the ROI. We are determining the precise location and dimensions of the detected speed bump.

???????C. Awareness Alerts

Based on the interpretations obtained from the algorithms, alerts and suggestions are provided to the driver and actions are recommended based on the potential problems.

VII. ACKNOWLEDGMENT

We would like to express our sincere gratitude to all individuals and organizations involved in this project. First to our guide Dr. Anusha Preetham for her insightful feedback and guidance during the research process. Secondly, to Prof. Mahanthesha U for encouraging the projects involving Digital Image Processing. Lastly, to our Artificial and Machine Learning Dept. for having a conducive environment to promote projects and research.

Conclusion

In conclusion, our road monitoring system offers significant advantages in improving road safety, traffic management, and lane detection. Its ability to provide real-time data, enhance safety, optimize traffic flow, and inform decision- making processes makes it a valuable tool for transportation authorities and road users alike. The research findings have demonstrated the system\'s ability to accurately collect and analyze data to optimize traffic management strategies, detect curve layouts on the road, determining lanes and provide timely warnings to drivers about potential risks and avoid congestion on roads. While this research is promising the benefits of this system, there may be challenges in terms of implementation, cost, and data management. Further studies and developments to include more features and develop a more interactive interface for user while maintaining the accuracy are essential to refine the system for releasing a prototype.

References

[1] Yanzhao Zu and Wei Qi Yan, “Traffic Sign Recognition based on Deep Learning,” Springer, March 7 2022. (references) [2] Satish Kumar Sattia, K. Suganya Devia, Prasenjit Dhara, P. Srinivasan, A Machine Learning Approach for detecting and tracking Road Lanes, ICT Express Vol 7 Issue 1, March 2021. [3] Rajesh Vand Rajshekhar JS, “Vision Based Traffic Sign Detection and Recognition using Artificial Intelligence,” iJRASET, July 2022. [4] Yueen Ma, Vincent Havyarimana, Jing Bai and Zhu Xiao, “Vision Based Lane Detection Marking Model Inference”, May 2019. [5] M. Swathi, K.V. Suresh, “Automatic Traffic Sign Detection and Recognition in Video Sequences,” 2nd IEEE International Conference, pp. 476-480, May 2017.

Copyright

Copyright © 2024 Naina Nimisha, Subrat Pandey, Siddhant Priyadarshi, Dr. Anusha Preetham. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.