语义分割--LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation

LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation

https://arxiv.org/abs/1707.03718

Torch7 code: https://github.com/e-lab/LinkNet

本文主要侧重语义分割的速度问题，算法思路类似 U-Net，引入了 residual blocks

LinkNet Architecture

conv means convolution 和 full-conv means full convolution
/2 denotes downsampling by a factor of 2 which is achieved by performing strided convolution 降采样2倍
∗2 means upsampling by a factor of 2 上采样2倍

输入图像首先使用一个 7*7的卷积核卷积，再用一个 步长为2的 3*3 spatial max-pooling

encoder-block 如下：

decoder-block 如下：

各个模块特征图数量：

首先来看看速度如何：

模型大小和运算量：

再来看看性能