Abstract
This paper presents a novel approach to real-time road lane-line detection using the Mask R-CNN framework, with the aim of enhancing the safety and efficiency of autonomous driving systems. Through extensive experimentation and analysis, the proposed system demonstrates robust performance in accurately detecting and segmenting lane boundaries under diverse driving conditions. Leveraging deep learning techniques, the system exhibits a high level of accuracy in handling complex scenarios, including variations in lighting conditions and occlusions. Real-time processing capabilities enable instantaneous feedback, contributing to improved driving safety and efficiency. However, challenges such as model generalizability, interpretability, computational efficiency, and resilience to adverse weather conditions remain to be addressed. Future research directions include optimizing the system's performance across different geographic regions and road types and enhancing its adaptability to adverse weather conditions. The findings presented in this paper contribute to the ongoing efforts to advance autonomous driving technology, with implications for improving road safety and transportation efficiency in real-world settings. The proposed system holds promise for practical deployment in autonomous vehicles, paving the way for safer and more efficient transportation systems in the future.