目的 为了提升彩色图像的分割精度，解决彩色图像分割中存在庞大计算成本和冗余参数的问题，本文提出一种双分支特征提取网络来解决上述问题。方法 双分支特征提取网络主要由语义信息分支和空间细节分支组成。语义信息分支通过在非对称残差模块中设置不同的空洞卷积率来获取输入图像不同尺度的上下文信息。空间细节分支是一个浅层且简单的网络，用于建立每个像素间的局部依赖关系以保留细节。在双分支之后连接一个特征聚合模块来有效地结合这2个分支的输出。结果 在没有任何预训练和后处理的情况下，在单块RTX2080Ti GPU上仅用0.91 M参数在Cityscapes数据集上以97帧/s的速度实现75.1%的分割准确性，在Camvid数据集上以107帧/s的推理速度取得了70.5%的分割效果。结论 通过大量实验证明，本文模型在分割准确性和效率之间取得了较好的平衡。

The work aims to propose a two-branch feature extraction network to improve the segmentation accuracy of color images and solve the problem of huge computing costs and redundant parameters in color image segmentation. The two-branch feature extraction network was mainly composed of semantic information branch and spatial detail branch. The semantic information branch obtained the context information of different scales of the input image by setting different hole convolution rates in the asymmetric residual module. The spatial detail branch was a shallow and simple network, which was used to establish the local dependency of each pixel to retain the details. A feature aggregation module was connected behind the two branches to effectively combine the outputs of the two branches. Without any pre-training and post-processing, on a single RTX2080Ti GPU, only 0.91 M parameters were used to achieve 75.1% segmentation accuracy on Cityscapes dataset at the speed of 97 FPS, and 70.5% segmentation effect was achieved on Camvid dataset at the reasoning speed of 107 FPS. A large number of experiments have proved that the proposed model achieves a good balance between segmentation accuracy and efficiency.