2024年3月8日 · 本文基于《SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation》论文，详细解读SegNet网络模型及其设计理念，并从基准测试的角度入手，对比其与其它典型分割模型的差别。

2017年1月2日 · We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable se

简介 SegNet是一个由剑桥大学团队开发的图像分割的开源项目，该项目可以对图像中的物体所在区域进行分割，例如车，马路，行人等，并且精确到像素级别。

2022年5月14日 · SegNet论文详解 本文提出了一种用于语义分割的深度全卷积神经网络结构SegNet，其核心由一个编码器网络和一个对应的解码器网络以及一个像素级分类层组成。 本文的创新在于： 解码器使用在对应编码器的最大池化步骤中计算的池化索引来执行非线性上采样，这 ...

本文作者总结了 FCN、 SegNet 、 U-Net 、FC-Densenet E-Net 和 Link-Net 、 RefineNet 、 PSPNet 、Mask-RCNN 以及一些半监督方法，例如 DecoupledNet 和 GAN-SS，并为其中的一些网络提供了 PyTorch 实现。在文章的最后一部分，作者总结了一些流行的数据集，并展示了一些网 …

2016年3月8日 · Segnet is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This is implementation of http://arxiv.org/pdf/1511.00561v2.pdf (Except for …

SegNet is used here to solve a binary pixel-wise image segmentation task, where positive samples (i.e. pixels that are assigned class of 1) represent cracks on the road, and negative samples (i.e. pixels that are assigned class of 0) represent normal road surface.

本文中提出了一种新颖实用的用于语义像素分割的深度全卷积神经网络架构-SegNet。SegNet由一个编码器网络、一个相应的解码器网络和一个像素分类层组成。编码器网络的架构在拓扑上与VGG16网络中的13个卷积层相同，去除了3个全连接层。

2015年5月27日 · Abstract: We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling. SegNet has several attractive properties; (i) it only requires forward evaluation of a fully learnt function to obtain smooth label predictions, (ii) with increasing depth, a larger context is considered for pixel labelling which improves accuracy ...

SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent.