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YOLOv8 中添加注意力机制 CBAM 具有多方面的好处

特征增强与选择

通道注意力方面

  突出重要特征通道：帮助模型自动学习不同通道特征的重要性权重。对于目标检测任务，某些通道可能携带了关于目标物体的关键信息，如颜色、纹理等特征。CBAM 的通道注意力模块可以增强这些重要通道的特征表示，让模型更加关注对目标检测有价值的特征，从而提高检测的准确性。例如，在检测车辆时，颜色通道中关于车辆独特颜色的信息通道权重会被提高，有助于模型更好地识别车辆。
  抑制无关特征通道：能够抑制那些对当前检测任务不太重要的通道特征，减少噪声和干扰信息的影响。这在复杂场景下尤为重要，可避免模型被背景或其他无关信息误导，提高模型的抗干扰能力。

空间注意力方面

  聚焦目标位置：空间注意力模块可以让模型关注特征图中不同位置的重要性。在目标检测中，能够突出目标物体所在的位置区域，使模型更加准确地定位目标。例如，当检测人群中的特定个体时，空间注意力会将焦点集中在该个体所在的区域，减少周围人群等其他区域的干扰。
  适应目标形状和大小变化：对于不同形状和大小的目标，空间注意力可以自适应地调整关注区域，更好地适应目标的变化。无论是检测小目标还是大目标，都能提高模型对目标的关注度和检测精度。

提高模型性能

  精度提升：通过强调重要的特征信息，CBAM 能够帮助 YOLOv8 更准确地识别和定位目标，从而提高模型的检测精度。在一些实验和实际应用中，添加 CBAM 后的 YOLOv8 在目标检测的准确率上有显著的提升。
  泛化能力增强：使模型更好地学习到数据中的关键特征，减少对特定数据分布的依赖，增强模型的泛化能力。这意味着在面对新的、未曾见过的场景或数据时，模型仍然能够保持较好的检测性能。

计算效率优化：

  特征筛选减少计算量：CBAM 在增强有用特征的同时，实际上也起到了一种特征筛选的作用。模型可以更加集中地处理重要的特征信息，减少对不必要信息的计算，从而在一定程度上提高计算效率，尤其是在处理大规模图像数据或实时检测任务时，这种优势更为明显。
  与 YOLOv8 结构互补：CBAM 的结构相对简单且轻量级，与 YOLOv8 的网络结构相契合。添加 CBAM 不会给模型带来过大的额外计算负担，能够在不显著增加模型复杂度的情况下提升性能。

yolov8增加CBAM具体步骤

CBAM代码

(1)在__init.py+conv.py文件的__all__内添加‘CBAM’

(2)conv.py文件复制粘贴CBAM代码

class ChannelAttention(nn.Module):"""Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet."""def __init__(self, channels: int) -> None:"""Initializes the class and sets the basic configurations and instance variables required."""super().__init__()self.pool = nn.AdaptiveAvgPool2d(1)self.fc = nn.Conv2d(channels, channels, 1, 1, 0, bias=True)self.act = nn.Sigmoid()def forward(self, x: torch.Tensor) -> torch.Tensor:"""Applies forward pass using activation on convolutions of the input, optionally using batch normalization."""return x * self.act(self.fc(self.pool(x)))class SpatialAttention(nn.Module):"""Spatial-attention module."""def __init__(self, kernel_size=7):"""Initialize Spatial-attention module with kernel size argument."""super().__init__()assert kernel_size in (3, 7), 'kernel size must be 3 or 7'padding = 3 if kernel_size == 7 else 1self.cv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)self.act = nn.Sigmoid()def forward(self, x):"""Apply channel and spatial attention on input for feature recalibration."""return x * self.act(self.cv1(torch.cat([torch.mean(x, 1, keepdim=True), torch.max(x, 1, keepdim=True)[0]], 1)))class CBAM(nn.Module):"""Convolutional Block Attention Module."""def __init__(self, c1, kernel_size=7):"""Initialize CBAM with given input channel (c1) and kernel size."""super().__init__()self.channel_attention = ChannelAttention(c1)self.spatial_attention = SpatialAttention(kernel_size)def forward(self, x):"""Applies the forward pass through C1 module."""return self.spatial_attention(self.channel_attention(x))

(3)修改task.py文件

  先引用刚导入的CBAM模块：

  再配置引用CBAM模块时的计算方法：

elif m is CBAM:c1,c2=ch[f],args[0]if c2!=nc:c2=make_divisible(min(c2,max_channels)*width,8)args=[c1,*args[1:]]

yolov8.yaml文件增加CBAM

yolov8.yaml

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parameters
nc: 80  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'# [depth, width, max_channels]n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPss: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPsm: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPsl: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs# YOLOv8.0n backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2- [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4- [-1, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32- [-1, 3, C2f, [1024, True]]- [-1, 1, SPPF, [1024, 5]]  # 9# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 6], 1, Concat, [1]]  # cat backbone P4- [-1, 3, C2f, [512]]  # 12- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 4], 1, Concat, [1]]  # cat backbone P3- [-1, 3, C2f, [256]]  # 15 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]]  # cat head P4- [-1, 3, C2f, [512]]  # 18 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]]  # cat head P5- [-1, 3, C2f, [1024]]  # 21 (P5/32-large)- [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)

yolov8.yaml增加CBAM

  yolov8.yaml增加CBAM步骤很简单只需要在卷积模块后面写上CBAM：

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parameters
nc: 80  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'# [depth, width, max_channels]n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPss: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPsm: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPsl: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs# YOLOv8.0n backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2- [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4- [-1, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32- [-1, 3, C2f, [1024, True]]- [-1, 1, SPPF, [1024, 5]]  # 9# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 6], 1, Concat, [1]]  # cat backbone P4- [-1, 3, C2f, [512]]  # 12- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 4], 1, Concat, [1]]  # cat backbone P3- [-1, 3, C2f, [256]]  # 15 (P3/8-small)- [-1, 1, CBAM, [256]]- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]]  # cat head P4- [-1, 3, C2f, [512]]  # 18 (P4/16-medium)- [-1, 1, CBAM, [512]]- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]]  # cat head P5- [-1, 3, C2f, [1024]]  # 21 (P5/32-large)- [-1, 1, CBAM, [1024]]- [[18, 21, 24], 1, Detect, [nc]]  # Detect(P3, P4, P5)

  从yaml文件可以看出来改进之前Neck部分的C2f模块后没有CBAM注意力机制，改进后的在最后三个C2f模块都添加了CBAM注意力机制，因此最后一层detect部分也需要增加3，结果是18, 21, 24
  运行示意：