联合注意力机制和多尺度特征的图像语义分割网络

Research on Semantic Image Segmentation Network Combining Attention Mechanism and Multi-Scale Features

针对卷积神经网络在图像语义分割时存在部分语义信息丢失、边界定位精度较低等问题,提出联合注意力机制和多尺度特征的卷积神经网络.首先基于注意力机制将网络提取到的多尺度特征进行加权融合,然后采用扩张卷积和全局平均池化聚合多尺度目标信息,最后采用边界精细粒度特征提取模块对分割边界进行优化.在多尺度PASCAL VOC2012和高分辨率Cityscapes数据集上的实验结果表明,所提网络的分割效果显著优于骨干网络ResNet-101,平均交并比分别提高12.2个百分点和9.3个百分点.

To address the problems of partial semantic information loss and low accuracy of boundary localization when convolutional neural networks are used for image semantic segmentation,this paper constructs a convolutional neural network by combining the attention mechanism and multi-scale features.The model firstly combines the multi-scale features extracted by the network based on the attention mechanism for weighting,then uses dilated convolution and global average pooling to aggregate the multi-scale target information,and finally uses the boundary fine-grained feature extraction module to optimize the segmentation boundary.Experimental results on the multi-scale PASCAL VOC2012 and high-resolution Cityscapes datasets show that the segmentation effect of the network in this paper is significantly better than that of the backbone ResNet-101,and the average cross-merge ratio is improved by 12.2 percentage points and 9.3 percentage points,respectively.