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.
