In this study, image processing-based real-time lane detection, which is one of the significant problems in autonomous vehicle control, is explored. A mobile robot platform is developed for that purpose. The motion of the mobile robot is provided by 4 direct current motors, which are independently controlled. An image processing code is developed in a Visual DSP 5.0 environment and run on a BF-561 processor embedded in the ADSP BF-561 EZ-KIT LITE evaluation board (Analog Devices). In the image processing algorithm, Hough lines obtained from the Hough transform of the captured images are called candidate lane marks. Various elimination methods are implemented on these candidate lane marks to detect the actual lane marks. Once the actual lane marks are determined, the real-world coordinates of these lane marks are computed using inverse perspective mapping. The heading angle of the mobile robot is then determined based on the position of the lane marks and the center of the mobile robot. The developed mobile robot platform and the lane detection algorithm are tested under various conditions, including dashed lane marks, varying lightening conditions, and the presence of one lane mark only. It is observed that the algorithm performs successfully and detects the lane marks even in the presence of various disturbance effects under these conditions. After the optimization of the developed image processing code, the number of frames processed by the algorithm increases from 3/s to 30/s, which should be satisfactory for real-time applications.