Lane detection is an important perception module in advanced driver-assistance and autonomous driving because it helps a vehicle interpret road geometry and remain centered within the lane. This paper presents a compact lane detection pipeline developed in Python with OpenCV using classical image-processing techniques. The method processes each video frame in sequence and applies grayscale conversion, Gaussian smoothing, Canny edge detection, a region-of-interest mask, and Probabilistic Hough Transform line extraction. The detected segments are separated into left- and right-lane candidates using slope-based rules, averaged to reduce noise, and drawn on the original frame to create an annotated road view. The system was tested on real driving video captured from a front-facing camera under normal daylight conditions. The results indicate that the approach performs well on straight roads and moderate curves when lane markings are visible, but its robustness decreases under shadows, glare, faded paint, and partial occlusion. Because the pipeline is lightweight, deterministic, and capable of near real-time execution on standard hardware, it is a useful baseline for educational and prototype intelligent transportation systems. The paper also discusses the problem context, design objectives, implementation steps, results, limitations, and future extensions such as adaptive thresholding, temporal tracking, and learning-based lane recognition.