yolov8改进c2f

### YOLOv8 中 C2F 特征融合方法的改进 #### 1. Context-Guided Network (CGNet) 为了提高YOLOv8在语义分割任务中的表现，可以引入Context-Guided Network (CGNet)，该网络通过上下文信息提取机制增强了特征表示能力[^1]。具体来说，CGNet的设计使得模型能够在保持轻量级的同时，在移动设备上实现高效的语义分割。 ```python class CGBlock(nn.Module): def __init__(self, in_channels, out_channels): super(CGBlock, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) self.norm = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU(inplace=True) def forward(self, x): return self.relu(self.norm(self.conv(x))) ``` #### 2. Spatial and Channel-wise Convolution (ScConv) 另一种改进方式是在C2f结构中集成Spatial and Channel-wise Convolution (ScConv)[^2]。这种卷积操作能够更好地捕捉空间和通道维度上的特征关系，从而提升目标检测的效果。ScConv被嵌入到C2f模块中作为瓶颈层的一部分，进一步优化了特征表达的质量。 ```python import torch.nn as nn class SCConv(nn.Module): def __init__(self, channels): super(SCConv, self).__init__() reduction_ratio = 16 # 定义空间注意力分支 self.spatial_attention = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(channels, channels // reduction_ratio, 1), nn.ReLU(), nn.Conv2d(channels // reduction_ratio, channels, 1), nn.Sigmoid() ) # 定义通道注意力分支 self.channel_attention = nn.Sequential( nn.Conv2d(channels, channels // reduction_ratio, 1), nn.ReLU(), nn.Conv2d(channels // reduction_ratio, channels, 1), nn.Sigmoid() ) def forward(self, x): spatial_att = self.spatial_attention(x).expand_as(x) channel_att = self.channel_attention(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) output = x * spatial_att * channel_att return output ``` #### 3. 组合应用 将上述两种技术结合起来应用于YOLOv8框架内，可以通过构建新的`C2f_ContextGuided`类来完成： ```python from yolov8.backbone import BackboneBase class C2f_ContextGuided(BackboneBase): def __init__(self, base_model_config): super(C2f_ContextGuided, self).__init__(base_model_config) self.cg_blocks = nn.ModuleList([CGBlock(base_model_config['in_channels'], base_model_config['out_channels'])]) self.sc_convs = nn.ModuleList([SCConv(base_model_config['channels'])]) def forward(self, inputs): features = [] for cg_block, sc_conv in zip(self.cg_blocks, self.sc_convs): feature = cg_block(inputs) enhanced_feature = sc_conv(feature) features.append(enhanced_feature) return tuple(features) ```