The Complex Environment Of The Road Detection Technology

Road Detection is one of the key techniques in vision-based vehicle guidance. This paper focus on road detection under complex environments, using color image segmentation algorithms, proposes road detection algorithms after analyzing and discussing several famous cases, which to a certain extent solves road region recognition problems under complex environments such as shadows, wet areas, brightness and leaves on unstructured roads.This paper reviews research on road detection at home and abroad by presenting several typical algorithms on structured road and several on unstructured road, pointing out that road detection technique on structured road has matured while the one on unstructured road is still in its infancy.In this paper, characteristic of road detection based on color image and several famous algorithms were discussed. Firstly, based on analyzing the character of road image and request on road detection, unstructured road were divided into two kinds vaguely, the width slowly varying road and the width acutely varying road, which is useful to propose efficient algorithm separately. Secondly, after having analyzed and experimented both the advantages and disadvantages of several classical image pre-processing techniques, a suitable image preprocessing stage was designed. Finally, several edge detection algorithms and region segmentation algorithms were also compared and analyzed.It also researched model based road detection techniques in this paper. An improved road detection algorithm based on linear model and Kalman filtering was proposed, which utilizes region of interest to limit detecting areas to save implement time. In the algorithm a recursive structure was used in which the current result depends on the previous result. Several pixels surrounding the central previous estimation of the road were randomly chosen and the HSI color features of these pixels were averaged to obtain the seed feature which was then used in a region growing image segmentation stage. Surrounding the left and right boundary of the road region several pixels were sampled and then Kalman filter were used to estimate straight lines of left and right boundary of road separately. The center of the road was calculated for the next iteration of seed feature estimation. In initial condition, it employs an unsupervised method matching to detect the boundary of road in the algorithm. Besides, special stage was added to reduce the effect of complex environments including shadows, wet areas and so on. Apart from above techniques, it also works over road detection technique based on color feature A road detection algorithm based on the fusion of edge and region features was proposed. Considered that the intensity is liable to be affected by environment outdoor, only hue and saturation region features were computed. An improved edge detection algorithm based on direction equalization sobel operator and local maximum edge strength entropy was chosen to detect the color edge of the road image. Integrating color edge features and region features after analyzing experiment data, viz. using edge features to revise error of region segmentation. Finally, chain codes tracking algorithm was adopted to track road region boundary. The experiment result indicates that the algorithm dose not lie on the width slowly varying assumption, so it could be used in both width slowly varying road and width acutely varying road. The algorithm has the ability to detection unstructured roads under complex environments such as shadows, wet areas and leaves on them.