融合累积分布函数和通道注意力机制的DeepLabV3+图像分割算法

DeepLabV3+image segmentation algorithm fusing cumulative distribution function and channel attention mechanism

为了解决DeepLabV3+在语义分割时未充分利用主干的低级特征,以及大倍数上采样造成有效特征缺失的问题,提出一种累积分布通道注意力DeepLabV3+(CDCA-DLV3+)模型。首先,基于累积分布函数和通道注意力提出了累积分布通道注意力(CDCA);然后,利用CDCA获取主干部分有效的低级特征;最后,采用特征金字塔网络(FPN)进行特征融合和逐步上采样,从而避免大倍数上采样所造成的特征损失。CDCA-DLV3+模型在Pascal VOC2012验证集与Cityscapes数据集上的平均交并比(mIoU)分别为80.09%和80.11%,相较于DeepLabV3+模型分别提升1.24和1.02个百分点。实验结果表明,所提模型分割结果更加精确。

In order to solve the problems that the low-level features of the backbone are not fully utilized,and the effective features are lost due to large-times upsampling in DeepLabV3+semantic segmentation,a Cumulative Distribution Channel Attention DeepLabV3+(CDCA-DLV3+)model was proposed.Firstly,a Cumulative Distribution Channel Attention(CDCA)was proposed based on the cumulative distribution function and channel attention.Then,the cumulative distribution channel attention was used to obtain the effective low-level features of the backbone part.Finally,the Feature Pyramid Network(FPN)was adopted for feature fusion and gradual upsampling to avoid the feature loss caused by largetimes upsampling.On validation set Pascal Visual Object Classes(VOC)2012 and dataset Cityscapes,the mean Intersection over Union(mIoU)of CDCA-DLV3+model was 80.09%and 80.11%respectively,which was 1.24 percentage points and 1.02 percentage points higher than that of DeepLabV3+model.Experimental results show that the proposed model has more accurate segmentation results.