自动驾驶论文: DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and RFB

mac2024-12-05 23

环视车位检测和车道线分割 DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block PDF: https://arxiv.org/pdf/1806.07226.pdf PyTorch: https://github.com/shanglianlm0525/PyTorch-Networks

1 概述:

DFNet主要划分为三块: 基本模块(basic module)、特征提取模块(features extraction module)、细化模块(refinement module).

1 选择Densenet作为基本模块(basic module);2 特征提取模块(features extraction module)由PSPNet提出的金字塔池模块(pyramid pooling module)后接卷积层和一个双线性上采样层组成.3 细化模块(refinement module)使用卷积层和池化层组成的残差融合块(residual fusion block, RFB) 减轻上采样带来的噪声干扰以及辨别处于类别边界上的点的归属.

2 网络结构

3 创新点

a 根据每个batch中的样本动态计算样本权重,权重计算公式如下: 其中,

w_{i}

是类别

w

的权重,

c

是类别数,

i (0, c)

α

和

β

分别是

w_{i}

的上下界阈值,避免权重差异过大,

N

是batch中的全部像素数,

n_{i}

是类别

i

的像素数,

when $n_{i}$ = 0, it means that the class i does not appear in this batch, we set the weight to 1. Because we need to increase the effect of small pixel number class on loss, so the smaller the $n_{i}$ , the larger the wi is. N and c are constant, wi is just changed by $n_{i}$ . When the $n_{i}$ is the average number, $w_{i}$ is calculated to be 1/2, the multiplicative coefficient of 1/2 is also used to decrease the $w_{i}$ of large pixel number of class.

b RFB中使用的结构(由卷积层和池化层组成), 实验表明结构(f)性能最好

4 实验效果展示

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