翻译：其中U-Net[63]在ResNet残差结构的基础上开发了将编码器与解码器之间连接起来的远跳连接，巧妙的桥接了编码端与解码端的语义鸿沟，在一段时期内，成为了最常用的语义分割模型。

Translation: U-Net[63] developed far-skip connections between the encoder and decoder based on the ResNet residual structure, ingeniously bridging the semantic gap between the encoding and decoding ends, and became the most commonly used semantic segmentation model for a period of time.

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Deep Residual U-Net

Deep Residual U-Net是一种基于U-Net和ResNet的图像分割网络。它采用了U-Net的编码器-解码器结构，同时在每个解码器块中使用了ResNet的残差块（DR块）来提高特征提取能力。DR块通过引入SE（Squeeze-and-Excitation）机制来增强编码器的全局特征提取能力，同时使用1×1卷积来改变特征图的维度，以确保3×3卷积滤波器不受前一层的影响。此外，为了避免网络太深的影响，在两组Conv 1×1-Conv 3×3操作之间引入了一条捷径，允许网络跳过可能导致性能下降的层，并将原始特征转移到更深的层。Deep Residual U-Net在多个图像分割任务中都取得了优秀的性能。

以下是Deep Residual U-Net的编码器-解码器结构示意图：

import torch.nn as nn

class DRBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DRBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(out_channels, out_channels, kernel_size=1)
        self.relu = nn.ReLU(inplace=True)
        self.se = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(out_channels, out_channels // 16, kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels // 16, out_channels, kernel_size=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.se(out) * out
        out += identity
        out = self.relu(out)
        return out

class DRUNet(nn.Module):
    def __init__(self, in_channels, out_channels, init_features=32):
        super(DRUNet, self).__init__()
        features = init_features
        self.encoder1 = nn.Sequential(
            nn.Conv2d(in_channels, features, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(features, features, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.encoder2 = nn.Sequential(
            DRBlock(features, features * 2),
            nn.Conv2d(features * 2, features * 2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 2, features * 2),
            nn.Conv2d(features * 2, features * 2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.encoder3 = nn.Sequential(
            DRBlock(features * 2, features * 4),
            nn.Conv2d(features * 4, features * 4, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 4, features * 4),
            nn.Conv2d(features * 4, features * 4, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.encoder4 = nn.Sequential(
            DRBlock(features * 4, features * 8),
            nn.Conv2d(features * 8, features * 8, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 8, features * 8),
            nn.Conv2d(features * 8, features * 8, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.bottleneck = nn.Sequential(
            DRBlock(features * 8, features * 16),
            nn.Conv2d(features * 16, features * 16, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 16, features * 16),
            nn.Conv2d(features * 16, features * 16, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )

        self.upconv4 = nn.ConvTranspose2d(features * 16, features * 8, kernel_size=2, stride=2)
        self.decoder4 = nn.Sequential(
            DRBlock(features * 16, features * 8),
            nn.Conv2d(features * 8, features * 8, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 8, features * 8),
            nn.Conv2d(features * 8, features * 8, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.upconv3 = nn.ConvTranspose2d(features * 8, features * 4, kernel_size=2, stride=2)
        self.decoder3 = nn.Sequential(
            DRBlock(features * 8, features * 4),
            nn.Conv2d(features * 4, features * 4, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 4, features * 4),
            nn.Conv2d(features * 4, features * 4, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.upconv2 = nn.ConvTranspose2d(features * 4, features * 2, kernel_size=2, stride=2)
        self.decoder2 = nn.Sequential(
            DRBlock(features * 4, features * 2),
            nn.Conv2d(features * 2, features * 2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 2, features * 2),
            nn.Conv2d(features * 2, features * 2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.upconv1 = nn.ConvTranspose2d(features * 2, features, kernel_size=2, stride=2)
        self.decoder1 = nn.Sequential(
            DRBlock(features * 2, features),
            nn.Conv2d(features, features, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features, features),
            nn.Conv2d(features, features, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )

        self.conv = nn.Conv2d(features, out_channels, kernel_size=1)

    def forward(self, x):
        enc1 = self.encoder1(x)
        enc2 = self.encoder2(self.pool1(enc1))
        enc3 = self.encoder3(self.pool2(enc2))
        enc4 = self.encoder4(self.pool3(enc3))

        bottleneck = self.bottleneck(self.pool4(enc4))

        dec4 = self.upconv4(bottleneck)
        dec4 = torch.cat((enc4, dec4), dim=1)
        dec4 = self.decoder4(dec4)
        dec3 = self.upconv3(dec4)
        dec3 = torch.cat((enc3, dec3), dim=1)
        dec3 = self.decoder3(dec3)
        dec2 = self.upconv2(dec3)
        dec2 = torch.cat((enc2, dec2), dim=1)
        dec2 = self.decoder2(dec2)
        dec1 = self.upconv1(dec2)
        dec1 = torch.cat((enc1, dec1), dim=1)
        dec1 = self.decoder1(dec1)

        return self.conv(dec1)

yolov5编码器解码器

YOLOv5（You Only Look Once version 5）是一种流行的实时物体检测算法，它基于Transformer架构，并结合了特征金字塔网络（FPN）。在YOLOv5中，编码器和解码器是非常关键的部分：

**编码器（Encoder）：**
编码器通常用于从原始输入图像开始，逐渐提取出高层次的、语义丰富的特征表示。YOLOv5的编码器部分采用ResNet或 CSPDarknet等预训练模型作为基础，如CSPDarknet53，通过卷积层和残差块对图像进行下采样，逐级降低分辨率但增加感受野，生成一系列特征图。

**解码器（Decoder）：**
解码器负责将编码后的特征图恢复到原始尺寸，同时保持足够的细节来进行目标检测。YOLOv5的解码器包含以下几个步骤：
1. **特征融合（Feature Fusion）**：将低层次特征与高层特征进行空间上或特征通道上的融合，增强特征表达能力。
2. **预测头（Prediction Heads）**：在每个特征级别上添加若干个预测头，每个头包含几个检测框的回归信息（包括边界框坐标）和分类概率。
3. **非极大抑制（Non-Max Suppression, NMS）**：最后，对所有层级的预测结果应用NMS算法，去除重叠度高的预测框，保留最终的目标检测结果。