Detecting traffic lanes is a real challenge, particularly under complex road conditions; indeed, during adverse weather conditions, the visibility of lane markings is poor, obscured, or often invisible. This paper investigates the application of computer vision techniques for effective lane line detection. We compare two methods: one utilizing Canny edge detection combined with Hough transform, and the other employing perspective transform along with the Sobel operator. The goal is to evaluate and compare their effectiveness in accurately detecting lane lines, thereby contributing to advancements in traffic management and autonomous driving technologies.