Track Detection System for Driving Using Image Processing and Deep Learning.

The development of intelligent motors heavily depends on lane detection. The paper proposes track detection to address issues like truncated detection accuracy of conventional techniques and poor real-time performance of sub-part of Machine Learning-based procedures. The proposed lane detection set of rules outperformed both traditional methods and Deep Learning-based methodologies in terms of accuracy, realtime performance, detection performance and interference resistance. Correct reputation cost and average processing time showed significant improvements. The provided set of guidelines is deemed to be essential for promoting the technological level of intelligent driver assistance and intelligent driver protection. [ABSTRACT FROM AUTHOR]