This study presents a quick and dependable method for detecting and classifying lane lines, focusing on the difference between solid and broken markings. The proposed technique aims to improve lane-keeping and overtaking help within Advanced Driver Assistance Systems (ADAS). It is designed with simplicity and efficiency in mind, ensuring real-time performance on affordable processors commonly found in modern vehicles. The system uses a series of computer vision algorithms that analyze raw RGB images to identify and categorize lane line segments as either solid or broken. Each part of the pipeline is clearly described, implemented, and tested using real-world road images and video sequences from a front-mounted camera. The strength and reliability of the proposed framework are evaluated in various road and lighting conditions. Additionally, the study discusses the benefits and drawbacks of the approach, emphasizing its potential for real-world use in driver assistance and autonomous driving.