Real-time Lane Detection Based on Image Edge Feature and Hough Transform

Lane detection systems are an important part of safe and secure driving by alerting the driver in the event of deviations from the main lane. Lane detection can also save the lifes of car occupants if they deviate from the road due to driver distraction.

In this paper, a real-time and illumination invariant lane detection method on high-speed video images is presented in three steps. In the first step, the necessary preprocessing including noise removal, image conversion from RGB colour to grey and the binarizing input image is done. Then, a polygon area as the region of interest is chosen in front of the vehicle to increase the processing speed. Finally, edges of the image in the region of interest are obtained with edge detection algorithm and then lanes on both sides of the vehicle are identified by using the Hough transform.

The implementation of the proposed method was performed on the IROADS database. The proposed method works well under different daylight conditions, such as sunny, snowy or rainy days and inside the tunnels. Implementation results show that the proposed algorithm has an average processing time of 28 milliseconds per frame and detection accuracy of 96.78%.

In this paper a straightforward method to identify road lines using the edge feature is described on high-speed video images.