Semantic segmentation of road scene based on dual-branch multi-scale feature fusion
Due to the imbalance between segmentation accuracy,model parameters,and inference speed in real-time scene semantic segmentation network models for scene images,a lightweight real-time semantic segmentation algorithm is proposed in this paper.Firstly,the algorithm employs a two-branch structure as the basic framework of the network,ex-tracting the feature information from different resolution feature maps in each branch.Secondly,improved pyramid split at-tention modules and residual atrous pyramid modules are incorporated into the high-resolution and low-resolution branches,respectively.Bilateral feature fusion is carried out between different branches to fully integrate spatial and semantic infor-mation.Finally,a feature fusion module is designed,and image semantic segmentation is achieved by restoring the image through upsampling operations.The algorithm achieves 76.8％and 70.5％mIoU with 5.02 M parameters and reaches up to 56 fps or 147 fps inference speed respectively in Cityscapes and Camvid datasets.
Image processingReal-time semantic segmentationLightweightAttention moduleMulti-scale features
NETL
NSTL
万方数据
维普
