Aiming to the challenge of poor pixel-consistency in inter-category and pixel-similarity in inter-category, in this paper, we propose an Encoder–Decoder network for image semantic segmentation using pooling SE-ResNet attention module, called PAEDN. It is an effective of attention mechanism to get aggregated information. According to the principle of SE-ResNet, a collection of Average, Maximum and Stochastic global pooling, which concentrate on contoured, detailed, and generalized information in a certain semantic segmentation, form attention modules. Channel Pooling Attention Module (CPAM) and Position Pooling Attention Module (PPAM) are designed and integrated into the Encoder to extract discriminative features from input images, and the Decoder is developed through SE-ResNet attention module to fuse the feature map in high-resolution with that in low-resolution. Experimental evaluations performed on the data sets PASCAL and Cityscapes, show the proposed Encoder–Decoder with pooling attention module produces good pixel-consistency semantic label, achieves 15.1% improvement to FCN.

针对类别间像素一致性差和类别间像素相似性差的挑战，在本文中，我们提出了一种使用池化 SE-ResNet 注意力模块进行图像语义分割的编码器 - 解码器网络，称为 PAEDN。获取聚合信息是一种有效的注意力机制。根据 SE-ResNet 的原理，Average、Maximum 和 Stochastic global pooling 的集合，集中在某个语义分割中的轮廓信息、详细信息和广义信息，形成注意力模块。通道池化注意力模块（CPAM）和位置池化注意力模块（PPAM）被设计并集成到编码器中，以从输入图像中提取判别特征，解码器是通过 SE-ResNet 注意力模块开发的，用于将高分辨率的特征图与低分辨率的特征图融合。对数据集 PASCAL 和 Cityscapes 进行的实验评估表明，所提出的带有池化注意力模块的编码器-解码器产生了良好的像素一致性语义标签，对 FCN 实现了 15.1% 的改进。