Aiming at the problem of ignoring normal attributes in road scene detection, in order to strengthen the use of spatial context and edge information, a road scene normal region segmentation method combined with spatial context algorithm was proposed, and the road scene was identified as the horizontal and the vertical regions corresponding to the road and the obstacles, respectively. On the basis of the cross-entropy loss function, the obstacle enhancement loss was added to improve the weight distribution of different categories in the training process, and improve the recognition rate of obstacles in small areas. The context improvement algorithm was proposed to optimize the matrix calculation method of the position association graph, reduce the space complexity and improve the calculation efficiency. The edge context module was embedded to reduce noise and strengthen the main edge, enhancing the use of edge information. Experimental results on the self-built dataset and the Cityscapes dataset show that compared with mainstream semantic segmentation methods, the proposed method strengthens the ability of network feature extraction, effectively improves the segmentation accuracy of the road normal region, of which the intersection is 2.1% higher than that of Deeplab, easily and effectively achieving obstacle avoidance tasks.