cbam注意力机制改进yoloV8

### 使用 CBAM 注意力机制改进 YOLOv8 模型

#### 修改配置文件

为了将CBAM注意力机制集成到YOLOv8模型中，首先需要创建一个新的配置文件`yolov8n-CBAM.yaml`。此文件基于原始的`yolov8.yaml`文件并进行了适当调整以支持CBAM模块[^3]。

# yolov8n-CBAM.yaml configuration file example
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.25  # layer channel multiple
...
backbone:
  ...
  [[-1, 1, 'Conv', [64, 3]], [-1, 1, 'CBAM', []], ... ]  # Add CBAM after Conv layers as needed.

#### 添加 CBAM 到网络结构

接下来，在YOLOv8源码目录下的相应位置引入CBAM类定义。通常是在`models/common.py`或其他类似的公共组件文件内增加CBAM实现代码片段[^4]：

import torch.nn.functional as F
from torch import nn


class ChannelAttention(nn.Module):
    def __init__(self, in_planes, ratio=16):
        super(ChannelAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)

        self.fc1   = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False)
        self.relu1 = nn.ReLU()
        self.fc2   = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False)

        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
        max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
        out = avg_out + max_out
        return self.sigmoid(out)


class SpatialAttention(nn.Module):
    def __init__(self, kernel_size=7):
        super(SpatialAttention, self).__init__()

        assert kernel_size in (3, 7), "kernel size must be 3 or 7"
        padding = 3 if kernel_size == 7 else 1

        self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = torch.mean(x, dim=1, keepdim=True)
        max_out, _ = torch.max(x, dim=1, keepdim=True)
        x = torch.cat([avg_out, max_out], dim=1)
        x = self.conv1(x)
        return self.sigmoid(x)


class CBAM(nn.Module):
    def __init__(self, gate_channels, reduction_ratio=16, no_spatial=False):
        super(CBAM, self).__init__()
        self.ChannelGate = ChannelAttention(gate_channels, reduction_ratio)
        self.no_spatial=no_spatial
        if not no_spatial:
            self.SpatialGate = SpatialAttention()

    def forward(self, x):
        x_out = self.ChannelGate(x)
        if not self.no_spatial:
            x_out = self.SpatialGate(x_out)
        return x * x_out

上述代码实现了完整的CBAM模块，包括通道注意力（Channel Attention）和空间注意力（Spatial Attention），并将两者组合起来形成最终的CBAM层[^2]。

#### 验证与训练

完成以上更改之后，可以按照常规流程编译项目、准备数据集，并启动训练过程验证新加入的CBAM特性是否有效提升了检测性能。建议对比未加CBAM前后的mAP指标变化情况来评估效果。