方向感知金字塔聚合网络的道路中心线提取

Direction-aware Pyramidal Aggregation Network for Road Centerline Extraction

道路中心线作为抽象类,无明确显性特征,进而造成模型无法准确提取道路中心线。针对该问题,文中将道路中心线提取建模为语义分割任务,并根据道路中心线的线性结构特点提出了一种方向感知金字塔聚合网络(Direction-aware Pyramidal Aggregation Network,DAPANet)。首先,针对道路中心线的空间分布特性及结构特点,设计了方向感知模块(Direction-aware Module,DAM),在主干网络(ResNet18)最后输出的4个层级上分别使用4个方向感知层提取道路中心线的方向特征。然后,进一步设计融合多向性特征的金字塔聚合模块(Pyramid Aggregation Module,PAM),融合4个层级提取到的结构特征,得到更具有鲁棒性的道路中心线特征。最后,在无人机平台下采集的真实数据上进行了实验,实验结果显示所提出的DAPANet取得了84.7%的mIoU和98.6%的Precision,道路中心线的IoU达到77.28%,性能优于其他先进的对比方法,证明了所提方法对提取道路中心线的有效性。

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.