Abstract: As an abstract class,road centerlines have no explicit features,which in turn causes the model fail to extract road centerlines accurately.To address this problem,this paper models road centerline extraction as a semantic segmentation task,and proposes a direction-aware pyramidal aggregation network(DAPANet) based on the spatial linear structure of road centerlines.Firstly,for the spatial distribution characteristics and structural features of road centerlines,this paper designs the direction-aware module(DAM) to extract the features of road centerlines using four direction-aware layers on each of the four layers of the final output of the backbone network(ResNet18).Then,it further designs the pyramid aggregation module(PAM) to fuse the structural features extracted from the four layers to obtain a more robust road centerline feature.Experiments are conducted on real data collected under the UAV platform,and the experimental results show that the proposed DAPANet achieves 84.7% of mIoU and 98.6% of Precision,in which the IoU of road centerline reaches 77.28%,outperforming other advanced comparative methods and proving the effectiveness of the proposed method.

Key words: Direction-awareness, Pyramid aggregation, Semantic segmentation, Road centerline extraction