基于空间特征提取和注意力机制的双路径语义分割

针对现阶段语义分割网络存在的空间和通道特征不匹配、小目标物体像素丢失等问题，设计了一种基于空间特征提取和注意力机制的双路径语义分割算法。空间信息路径利用四倍下采样来保留高分辨率特征，并引入空间特征提取模块融合多尺度空间信息，加强网络对小目标物体的识别能力；采用一条结合双阶通道注意力的语义上下文路径提取判别特征，使深层特征能够指导浅层特征捕捉更精确的语义信息，从而降低精度损失。在CamVid和Aeroscapes数据集上验证该算法，平均交并比分别可达70.5%和51.8%,相比于当前主流的双路径语义分割模型有所提升，结果验证了所提算法的有效性。

Dual-path semantic segmentation based on spatial feature extraction and attention mechanism

Aiming at the problems of the current semantic segmentation network''s spatial and channel feature mismatch, as well as the pixel loss of small target objects, this paper designed a dual-path semantic segmentation algorithm based on spatial feature extraction and attention mechanism. The spatial information path used four times downsampling to retain high-resolution features, and introduced a spatial feature extraction module to fuse multi-scale spatial information, thereby strengthening the network''s ability to recognize small target objects. In addition, it used a semantic context path combined with two-stage channel attention to extract discriminative features, so that deep features could guide shallow features to capture more accurate semantic information, thereby reducing accuracy loss. This paper verified the algorithm on the CamVid dataset and Aeroscapes dataset, the mean intersection over union can reach 70.5% and 51.8% respectively. Compared with the current mainstream dual-path semantic segmentation model, the results verify the effectiveness of the proposed algorithm.