YOLOv11融合FFCA-YOLO中的FEM模块以改进小目标检测

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YOLOv11融合FFCA-YOLO中的FEM模块以改进小目标检测

引用

CSDN

https://blog.csdn.net/StopAndGoyyy/article/details/143866491

FFCA-YOLO论文概述

论文《FFCA-YOLO for Small Object Detection in Remote Sensing Images》提出了一种针对遥感图像中小目标检测的高效检测器——FFCA-YOLO。该检测器通过引入三个创新模块来解决特征表示不足和背景混淆等问题：

特征增强模块（FEM）：用于增强局部特征感知能力。
特征融合模块（FFM）：用于实现多尺度特征融合。
空间上下文感知模块（SCAM）：用于增强全局关联能力。

此外，为了优化计算效率，论文还提出了基于部分卷积（PConv）的轻量级版本L-FFCA-YOLO。

将FEM模块融入YOLOv11

1. 创建脚本文件

在ultralytics->nn路径下创建blocks.py脚本，用于存放FEM模块的代码。

2. 复制代码

将FEM模块的代码复制到blocks.py中：

import torch
import torch.nn as nn
from ultralytics.nn.modules.conv import Conv

class BasicConv_FFCA(nn.Module):
    def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True,
                 bn=True, bias=False):
        super(BasicConv_FFCA, self).__init__()
        self.out_channels = out_planes
        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
                              dilation=dilation, groups=groups, bias=bias)
        self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True) if bn else None
        self.relu = nn.ReLU(inplace=True) if relu else None

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.relu is not None:
            x = self.relu(x)
        return x

class FEM(nn.Module):
    def __init__(self, in_planes, out_planes, stride=1, scale=0.1, map_reduce=8):
        super(FEM, self).__init__()
        self.scale = scale
        self.out_channels = out_planes
        inter_planes = in_planes // map_reduce
        self.branch0 = nn.Sequential(
            BasicConv_FFCA(in_planes, 2 * inter_planes, kernel_size=1, stride=stride),
            BasicConv_FFCA(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=1, relu=False)
        )
        self.branch1 = nn.Sequential(
            BasicConv_FFCA(in_planes, inter_planes, kernel_size=1, stride=1),
            BasicConv_FFCA(inter_planes, (inter_planes // 2) * 3, kernel_size=(1, 3), stride=stride, padding=(0, 1)),
            BasicConv_FFCA((inter_planes // 2) * 3, 2 * inter_planes, kernel_size=(3, 1), stride=stride, padding=(1, 0)),
            BasicConv_FFCA(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=5, dilation=5, relu=False)
        )
        self.branch2 = nn.Sequential(
            BasicConv_FFCA(in_planes, inter_planes, kernel_size=1, stride=1),
            BasicConv_FFCA(inter_planes, (inter_planes // 2) * 3, kernel_size=(3, 1), stride=stride, padding=(1, 0)),
            BasicConv_FFCA((inter_planes // 2) * 3, 2 * inter_planes, kernel_size=(1, 3), stride=stride, padding=(0, 1)),
            BasicConv_FFCA(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=5, dilation=5, relu=False)
        )
        self.ConvLinear = BasicConv_FFCA(6 * inter_planes, out_planes, kernel_size=1, stride=1, relu=False)
        self.shortcut = BasicConv_FFCA(in_planes, out_planes, kernel_size=1, stride=stride, relu=False)
        self.relu = nn.ReLU(inplace=False)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        out = torch.cat((x0, x1, x2), 1)
        out = self.ConvLinear(out)
        short = self.shortcut(x)
        out = out * self.scale + short
        out = self.relu(out)
        return out

3. 更改task.py文件

在ultralytics->nn->modules->task.py中导入FEM模块：

from ultralytics.nn.blocks import *

在模型解析函数parse_model中添加FEM模块的解析代码：

elif m is FEM:
    c2 = args[0]
    args = [ch[f], *args]

4. 更改yaml文件

打开ultralytics/cfg/models/11/yolov11.yaml文件，替换原有模块：

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 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=yolo11n.yaml' will call yolo11.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
![](https://wy-static.wenxiaobai.com/chat-rag-image/91658219121794859)
  s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
  m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
  l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
  x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs
# YOLO11n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 2, FEM, [512]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 2, C3k2, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 2, C2PSA, [1024]] # 10
# YOLO11n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, C3k2, [512, False]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)

5. 修改train.py文件

创建训练脚本：

from ultralytics.models import YOLO
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'

if __name__ == '__main__':
    model = YOLO(model='ultralytics/cfg/models/11/yolo11.yaml')
    # model.load('yolov8n.pt')
    model.train(data='./data.yaml', epochs=2, batch=1, device='0', imgsz=640, workers=2, cache=False,
                amp=True, mosaic=False, project='runs/train', name='exp')

按照上述步骤修改后，即可开始训练模型。