Detection of Lane & Speed Breakers for Autonomous Vehicles using CNN

Abstract

Abstract: The advancement of autonomous vehicles necessitates robust systems for real-time detection of lane markings and speed breakers to ensure safe and efficient navigation. In this paper, we propose a novel approach for lane and speed breaker detection utilizing Convolutional Neural Networks (CNN). Our method achieves remarkable accuracy in identifying lane markings and speed breakers from input images captured by onboard cameras. By leveraging the capabilities of CNN, our system demonstrates superior performance in diverse environmental conditions, including varying lighting, weather, and road surface conditions. We present a comprehensive analysis of the CNN architecture employed in our system, highlighting its ability to extract meaningful features from raw image data and make accurate predictions. The training process involves a large dataset comprising annotated images of lane markings and speed breakers, enabling the CNN to learn discriminative patterns for effective detection. Furthermore, we evaluate the performance of our proposed system through extensive experimentation, quantifying its accuracy, precision, and recall rates across different scenarios. The results indicate the effectiveness and reliability of our approach in real-world settings, showcasing its potential for integration into autonomous vehicle platforms. Overall, this paper contributes to the field of autonomous driving by presenting a robust and efficient solution for lane and speed breaker detection, leveraging the power of machine learning algorithms, specifically CNN, to enhance the safety and autonomy of vehicles on the road.