Notice of Retraction: Improvement of lane marks extraction technique under different road conditions

Abstract

Ahstract-The application of edge detection to obtain salient lane boundaries on road image is popular in the computer vision area. However, edge detectors may be easily distracted by manifold noises that emerged on the road surface such as shadow cast, vehicles or other obstacles. Additionally, sky region can adversely affect the performance of lane detection method due to the presence of horizontal edges in the sky region. In this paper, lane preprocessing approach is proposed to effectively extract lane marks from the traffic scene. Horizon localization is improved to automatically segment the sky and road region through regional minimum search. It is followed by the lane region analysis to adaptively remove the road pixels and obstacles. Evaluations on the proposed lane preprocessing method are performed under different time of the day/night and various road sceneries such as city, highway, rural, suburban etc. Some results and findings are shown in the paper with regards on the observation of the road intensity change respective to the variation of the road conditions.

Model-Based Robust Lane Detection for Driver Assistance

“…In this paper, we develop a simple but efficient horizon localization based on the notion of the horizon line proposed in [4]. The horizon line divides the scene into the sky region and the road region.…”

“…As suggested as in [4], to enhance the effect of of searching the first minimum value along the vertical mean distribution curve, the curve is divided into n segments to obtain regional minimum (in this paper, n is chosen to be 10). A regional minimum S is represented as (m, p), where m is the average of gray values of the row where the regional minimum occurs, and p is the number of the row.…”

“…[18] tested their algorithm on many datasets including some of CMU/VASC datasets. [3,4,[8][9][10][11][12][13][14][15][16][17] tested their algorithm on their own home-made datasets, and did not publish the Any works of [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] do not provide the criterion to decide whether the lane is detected or not. In this paper, we determine that the lane is detected when the detected lane turns out to follow the real lane reasonably well by human eyes.…”

“…Lane detection systems are based on various sensors including camera sensors, internal vehiclestate sensors, line sensors, LASER radio detection and ranging (RADAR) sensors, global positioning system (GPS) sensors, and so on. Since the camera sensor-based one can operate under various environments whereas the other lane detection systems work well under some special environments, research interests about the lane detection have been concentrated more on vision(camera sensor)based systems during the past two decades [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

“…In addition to these, neighborhood vehicles, trees or buildings can cast shadows over lanes or the other vehicles or obstacles can occlude the lanes and distract the tracking system. Most of vision-based lane detection works in the past two decades [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] have focused on dealing with such difficulties of the road environments.…”