YOLOv8 AKConv轻量级架构下的高效检测

### 集成AKConv层至YOLOv11 #### 设计思路 在YOLOv11中集成AKConv(Adaptive Kernel Convolution)，旨在通过引入自适应卷积核机制提升模型效率与准确性。此过程涉及对原有网络结构的调整，特别是针对特征提取和融合阶段。 #### 修改配置文件 首先，在YOLOv11项目的`config.py`或其他相应配置文件内定义新的组件参数设置[^1]： ```python # config.py or equivalent configuration file akconv_settings = { 'in_channels': 256, 'out_channels_1': 128, 'out_channels_2': 64, } ``` #### 定义AKConv类 接着创建一个新的Python脚本用于定义AKConv类及其初始化逻辑。该操作基于已有的优化版本进行扩展以适配最新版框架特性[^2]: ```python import torch.nn as nn class AKConvOptimized(nn.Module): def __init__(self, in_channels, out_channels): super(AKConvOptimized, self).__init__() # Define layers here based on the optimized structure from previous versions. pass def forward(self, x): # Implement forward propagation logic using adaptive kernel convolution mechanism. return x ``` #### 更新YOLOv11头部模块 随后需更新YOLOv11的头部(`head`)部分来包含新加入的AKConv实例化对象。这一步骤类似于之前提到的方法但在具体实现细节上有差异[^3]: ```python from .custom_layers import AKConvOptimized class YOLOv11Head(nn.Module): def __init__(self, in_channels, num_classes): super(YOLOv11Head, self).__init__() self.akconv1 = AKConvOptimized(in_channels=in_channels, out_channels=akconv_settings['out_channels_1']) self.akconv2 = AKConvOptimized(in_channels=akconv_settings['out_channels_1'], out_channels=akconv_settings['out_channels_2']) self.conv_final = nn.Conv2d(akconv_settings['out_channels_2'], num_classes, kernel_size=(1, 1)) def forward(self, inputs): outputs = self.akconv1(inputs) outputs = self.akconv2(outputs) outputs = self.conv_final(outputs) return outputs ``` #### 测试与验证 完成上述更改之后，应当利用官方提供的预训练权重以及公开可用的数据集来进行初步测试，确保新增加的功能不会破坏现有功能的同时也能够带来预期之外的效果增益。