一种双分支结构的图像语义分割算法

图像语义分割需要精细的细节信息和丰富的语义信息，然而在特征提取阶段，连续下采样操作会导致图像中物体的空间细节信息丢失。为解决该问题，提出一种双分支结构语义分割算法，在特征提取阶段既能有效获取丰富的语义信息又能减少物体细节信息的丢失。该算法的一个分支使用浅层网络保留高分辨率细节信息有助于物体的边缘分割，另一个分支使用深层网络进行下采样获取语义信息有助于物体的类别识别，再将两种信息有效融合可以生成精确的像素预测。通过Cityscapes数据集和CamVid数据集上的实验验证，与现有语义分割算法相比，所提算法在较少的参数条件下，获得了较好的分割效果。

An image semantic segmentation algorithm with a two-branch structure

Image semantic segmentation requires fine detail information and rich semantic information, but in the stage of feature extraction, continuous down-sampling operation will lead to the loss of spatial details of objects in the image. To solve this problem, a semantic segmentation algorithm based on double-branch structure is proposed, which can obtain rich semantic information effectively and reduce the loss of object details in feature extraction stage. One branch of the algorithm uses shallow network to retain high-resolution detail information which is helpful for object edge segmentation, and the other branch uses deep network for downsampling to obtain semantic information which is helpful for object category recognition, and then the effective fusion of the two kinds of information can generate accurate pixel prediction. Experimental results on Cityscapes and CamVid datasets show that the proposed algorithm achieves better segmentation performance under fewer parameters than existing semantic segmentation algorithms.