YOLOV8增加rfb-s模块

### 如何在YOLOv8中集成RFB-S模块

#### 添加RFB-S模块至YOLOv8网络架构

要在YOLOv8中引入RFB-S（Receptive Field Block Single）模块，需修改模型定义文件并调整训练配置。具体操作如下：

1. **构建RFB-S模块**

定义一个新的Python类`RFBSModule`继承自`torch.nn.Module`，实现RFB-S特有的多分支卷积结构以及膨胀卷积机制[^1]。

   import torch
   from torch import nn
   
   class RFBSModule(nn.Module):
       def __init__(self, in_channels, out_channels):
           super(RFBSModule, self).__init__()
           
           inter_channels = int(out_channels / 4)
   
           # Branches with different kernel sizes and dilation rates
           self.branch_0 = nn.Conv2d(in_channels=in_channels,
                                     out_channels=inter_channels,
                                     kernel_size=(1, 1),
                                     stride=(1, 1))
   
           self.branch_1 = nn.Sequential(
               nn.Conv2d(in_channels=in_channels,
                         out_channels=inter_channels,
                         kernel_size=(1, 1)),
               nn.ReLU(),
               nn.Conv2d(inter_channels,
                         inter_channels,
                         kernel_size=(3, 3),
                         padding=1,
                         dilation=1)
           )
   
           self.branch_2 = nn.Sequential(
               nn.Conv2d(in_channels=in_channels,
                         out_channels=inter_channels,
                         kernel_size=(1, 1), 
                         bias=False),
               nn.ReLU(),
               nn.Conv2d(inter_channels,
                         inter_channels,
                         kernel_size=(3, 3),
                         padding=3,
                         dilation=3,
                         bias=False)
           )

           self.conv_linear = nn.Conv2d(3 * inter_channels,
                                        out_channels=out_channels,
                                        kernel_size=(1, 1))

       def forward(self, x):
           branch_features = [
               self.branch_0(x),
               self.branch_1(x),
               self.branch_2(x)]
   
           concat_features = torch.cat(branch_features, dim=1)

           output = self.conv_linear(concat_features)
           return output

2. **替换原有特征提取层**

修改YOLOv8的骨干网部分，在适当位置插入上述创建好的RFB-S组件代替原有的标准卷积层或瓶颈单元。

3. **更新配置参数**

调整超参设置以适应新加入的复杂度更高的子网设计，比如学习率衰减策略、正则化强度等[^2]。

4. **验证改进效果**

使用预处理过的数据集重新训练带有增强版特征金字塔的检测器，并对比未改动前后的性能差异，评估RFB-S带来的增益情况[^4]。