Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (9): 255-261.DOI: 10.3778/j.issn.1002-8331.2202-0208

• Graphics and Image Processing • Previous Articles     Next Articles

Curb Segmentation Using Dual Branch and Feature Fusion Network

SUN Yang, HAN Lei, WANG Chengqing, LI Yunpeng   

  1. 1.School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan, Hebei 056000, China
    2.Key Laboratory of Intelligent Vehicles, Handan, Hebei 056000, China
  • Online:2023-05-01 Published:2023-05-01

采用双支路与特征融合网络的路沿分割

孙扬,韩磊,王程庆,李韵鹏   

  1. 1.河北工程大学 机械与装备工程学院,河北 邯郸 056000
    2.邯郸市智能车辆重点实验室,河北 邯郸 056000

Abstract: Curb detection is an important goal of intelligent vehicle environmental perception. This paper uses semantic segmentation to detect curbobject. Aiming at the problem that the semantic segmentation network cannot balance shallow features and deep features, a real-time curb segmentation network with dual-branch feature fusion is designed. The main branch of the network uses the residual structure module for downsampling, and reverts to the original resolution when the feature map resolution is 1/16 of the input resolution. Multiple modules are used to fuse shallow spatial features and high-level semantic features. The SDFE(spatial detail feature extraction)module is used to make up for the loss of geometric features. The joint feature pyramid(JFP)module is used to have strong semantics in multiple stages of the network. The multi-scale features of information are used in combination, the spatial feature attention mechanism(feature attention, FA)is designed in the branch, and 4 convolution normalization is used to enhance the extraction of spatial detail features based on the deal with the attention. FFM(feature fusion module)  is designed. The module fuses high-level semantic features with shallow features. The performance of the network is evaluated. The network test mIoU is 79.65% and the FPS is 59.6. The experiment is carried out on the road, and the segmentation effect is fast and good.

Key words: curb segmentation, dual branch, features fusion

摘要: 路沿检测是智能车辆环境感知的重要目标,使用语义分割的方法对路沿目标进行检测。针对语义分割网络不能平衡浅层特征和深度特征的问题,设计了一种具有双支路特征融合的实时路沿分割网络。网络主支使用残差结构模块进行下采样,在特征图分辨率为输入分辨率的1/16时恢复至原来分辨率。采用多个模块来融合浅层空间特征与高级语义特征:使用SDFE(spatial detail feature extraction)模块弥补几何特征的丢失;使用联合特征金字塔(joint feature pyramid,JFP)模块将网络多个阶段具有强语义信息的多尺度特征结合使用;支路中设计了特征注意力机制(feature attention,FA)模块,使用4个卷积归一化,通过注意力模块处理,用来增强空间细节特征的提取;设计了FFM(feature fusion module)模块融合高级语义特征与浅层特征。对网络进行性能评价,该网络测试mIoU为79.65%,FPS为59.6,在道路上进行实车实验,分割快速且效果良好。

关键词: 路沿分割, 双支路, 特征融合