yolo11小目标识别改进

YOLO11在小目标识别方面有多种改进方法。一方面，它本身作为一款尖端且先进的模型，在之前YOLO版本成功的基础上构建，引入了新功能和改进，设计上快速、准确且易于使用，可提升各种目标检测任务包括小目标识别的性能和灵活性，适用于物体检测和跟踪、实例分割、图像分类以及姿态估计等任务，从整体架构和设计上为小目标识别提供了更好的基础[^1]。 另一方面，为进一步优化小目标检测精度，可加入CBAM（Convolutional Block Attention Module）注意力机制。CBAM通过在空间和通道两个维度上强调重要特征，有助于模型更加专注于关键区域，能有效提高小目标检测的准确率[^3]。 ```python # 以下为简单示意如何在代码中加入CBAM模块（伪代码） import torch import torch.nn as 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, in_planes): super(CBAM, self).__init__() self.ca = ChannelAttention(in_planes) self.sa = SpatialAttention() def forward(self, x): x = self.ca(x) * x x = self.sa(x) * x return x # 假设YOLO11某个层需要加入CBAM # 这里简单示意在一个卷积层后加入CBAM class YOLO11WithCBAM(nn.Module): def __init__(self): super(YOLO11WithCBAM, self).__init__() self.conv = nn.Conv2d(3, 64, kernel_size=3, padding=1) self.cbam = CBAM(64) def forward(self, x): x = self.conv(x) x = self.cbam(x) return x ```