基于改进空间通道信息的全局烟雾注意网络

Improved spatial and channel information based global smoke attention network

针对烟雾因半透明、形状不规则和边界模糊造成分割困难的问题,提出了基于注意力机制的长距离信息建模方法,以提取长距离像素间的依赖和连续性关系。通过注意力机制作用原理,解决孤立小块区域误分类问题,减少非连续区域的烟雾误判。为避免注意力网络大尺寸矩阵运算造成的内存和计算负担,对空间和通道2种注意力方式进行改进,分别设计了双向定位空间注意力(BDA)模块和多尺度通道注意力(MSCA)融合模块,弥补现有注意力全局池化操作导致的大量空间信息丢失。将所提注意力模块和残差深度网络合并,构建面向图像烟雾分割的全局烟雾注意网络,在尽可能不丢失全局信息相关性的同时减少内存消耗。实验结果表明:所提网络在DS01、DS02、DS03合成烟雾测试集上,取得的平均交并比分别为73.13%、73.81%、74.25%,总体上优于对比算法。

Smoke has the characteristics of semi-transparency,irregularity and blurry boundaries,leading to the challenging task of image smoke segmentation.To solve these problems,we propose an attention modeling method to extract the correlation of long-distance information.The attention method can capture the long-distance dependency of pixels and continuity of regions,so as to reduce the misclassification of discontinuous smoke regions.To avoid large memory consumption of large matrix multiplication and high computational complexity,we modify both spatial and channel attention structures to design a bi-direction attention(BDA)and a multi-scale channel attention(MSCA),which are used to compensate for lost spatial information by global pooling in attention methods.In addition,we propose a global smoke attention network,which combines residual networks with attention models to reduce memory consumption and computational complexity without sacrificing global correlation information.Experimental results show that the proposed network achieves the mean intersection over union of 73.13%,73.81%and 74.25%on the three virtual smoke test datasets of DS01,DS02 and DS03,respectively,and it outperforms most of the existing state-of-the-art methods.