基于空洞空间池化金字塔的自动驾驶图像语义分割方法

If a vehicle can accurately and quickly understand the semantics of people and vehicles on the road，it can guide the obstacle avoidance and path planning to a large extent. The existing semantic segmentation methods based on deep learning need a tradeoff between segmentation speed and segmentation accuracy. In this paper，based on the existing semantic segmentation network，the multi-scale semantic information of image can be obtained by adding an atrous spatial pyramid pooling structure after the reference network of feature extraction. Experimental results show that modules A_ASPP_1 and A_ASPP_2 proposed can effectively segment images of common people and various vehicles in automatic driving scenes. Compared with BiSeNet，two corresponding improved network structures have 2.1 and 1.2 percentage points higher mean intersection over union of training results respectively，though with a little lower segmentation speed.