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YOLOv11模型改进-模块-引入多尺度前馈网络MSFN 用于解决噪声

article 2024/12/23 16:19:54

本篇文章将介绍一个新的改进机制——多尺度前馈网络MSFN，并阐述如何将其应用于YOLOv11中，显著提升模型性能。首先，多尺度前馈网络（Multi-scale Feed-forward Network，MSFN）是一种用于提升特征处理能力和效果的网络结构，在相关研究中被用于高光谱图像去噪任务。随后，我们将详细讨论他的模型结构，以及如何将MSFN 模块与YOLOv11相结合，以提升目标检测的性能。

1. 多尺度前馈网络MSFN结构介绍

它主要是为了解决 Transformer 中原始前馈网络（FFN）单尺度特征聚合的局限性。原始 FFN 在进行特征处理时，仅从单一尺度聚合信息，所包含的信息有限，无法充分挖掘和利用图像中的多尺度特征，从而影响了去噪等任务的性能。

1. 通道扩展：采用两个卷积来扩展特征通道。这种操作可以增加特征的维度，为后续的多尺度处理提供更丰富的信息基础。扩展比率，意味着经过这两个卷积操作后，特征通道数量会按照相应比例增加。

2. 并行路径与门控机制：输入特征在两条并行路径中进行处理。引入门控机制来增强非线性变换。具体通过两条路径特征的逐元素乘积实现。这个机制能够动态地调整不同路径特征的贡献，使得网络能够更好地适应不同的输入特征和任务需求。

3. 特征提取：在较低路径中，使用深度卷积进行特征提取。深度卷积可以对每个通道单独进行卷积操作，有效地提取局部特征，同时减少计算量。在较高路径中，采用多尺度扩张卷积进行多尺度特征提取。这里使用了两个扩张率分别为 2 和 3 的扩张卷积。扩张卷积通过在卷积核元素之间插入空格的方式，扩大了卷积核的感受野，能够在不增加参数量的情况下获取更广泛的上下文信息，从而实现多尺度特征的提取。

2. YOLOv11与MSFN的结合

本文将YOLOv11模型的C2PSA模块中的ffn层替换MSFN，组合成C2PSA_MSFN模块，解决 C2PSA模块中原始前馈网络（FFN）单尺度特征聚合的局限性

3. MSFN代码部分

import torch.nn as nn
import torch.nn.functional as F
from .block import PSABlock,C2PSA

## Multi-Scale Feed-Forward Network (MSFN)
class FeedForward(nn.Module):
    def __init__(self, dim, ffn_expansion_factor = 2.66, bias = False,):
        super(FeedForward, self).__init__()

        hidden_features = int(dim*ffn_expansion_factor)

        self.project_in = nn.Conv3d(dim, hidden_features*3, kernel_size=(1,1,1), bias=bias)

        self.dwconv1 = nn.Conv3d(hidden_features, hidden_features, kernel_size=(3,3,3), stride=1, dilation=1, padding=1, groups=hidden_features, bias=bias)
        # self.dwconv2 = nn.Conv3d(hidden_features, hidden_features, kernel_size=(3,3,3), stride=1, dilation=2, padding=2, groups=hidden_features, bias=bias)
        # self.dwconv3 = nn.Conv3d(hidden_features, hidden_features, kernel_size=(3,3,3), stride=1, dilation=3, padding=3, groups=hidden_features, bias=bias)
        self.dwconv2 = nn.Conv2d(hidden_features, hidden_features, kernel_size=(3,3), stride=1, dilation=2, padding=2, groups=hidden_features, bias=bias)
        self.dwconv3 = nn.Conv2d(hidden_features, hidden_features, kernel_size=(3,3), stride=1, dilation=3, padding=3, groups=hidden_features, bias=bias)


        self.project_out = nn.Conv3d(hidden_features, dim, kernel_size=(1,1,1), bias=bias)

    def forward(self, x):
        x = x.unsqueeze(2)
        x = self.project_in(x)
        x1,x2,x3 = x.chunk(3, dim=1)
        x1 = self.dwconv1(x1).squeeze(2)
        x2 = self.dwconv2(x2.squeeze(2))
        x3 = self.dwconv3(x3.squeeze(2))
        # x1 = self.dwconv1(x1)
        # x2 = self.dwconv2(x2)
        # x3 = self.dwconv3(x3)
        x = F.gelu(x1)*x2*x3
        x = x.unsqueeze(2)
        x = self.project_out(x)
        x = x.squeeze(2)
        return x


class PSABlock_MSFN(PSABlock):


    def __init__(self, c, qk_dim =16 , pdim=32, shortcut=True) -> None:
        """Initializes the PSABlock with attention and feed-forward layers for enhanced feature extraction."""
        super().__init__(c)

        self.ffn = FeedForward(c)



class C2PSA_MSFN(C2PSA):

    def __init__(self, c1, c2, n=1, e=0.5):
        """Initializes the C2PSA module with specified input/output channels, number of layers, and expansion ratio."""
        super().__init__(c1, c2)
        assert c1 == c2
        self.c = int(c1 * e)

        self.m = nn.Sequential(*(PSABlock_MSFN(self.c, qk_dim =16 , pdim=32) for _ in range(n)))

4. 将MSFN引入到YOLOv11中

第一: 将下面的核心代码复制到D:\bilibili\model\YOLO11\ultralytics-main\ultralytics\nn路径下，如下图所示。

第二：在task.py中导入MSFN包

第三：在task.py中的模型配置部分下面代码

第四：将模型配置文件复制到YOLOV11.YAMY文件中

# 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
  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, C3k2, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
  - [-1, 2, C3k2, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]] # 9
  - [-1, 2, C2PSA_MSFN, [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)

第五：运行成功


from ultralytics.models import NAS, RTDETR, SAM, YOLO, FastSAM, YOLOWorld

if __name__=="__main__":


    # 使用自己的YOLOv11.yamy文件搭建模型并加载预训练权重训练模型
    model = YOLO(r"D:\bilibili\model\YOLO11\ultralytics-main\ultralytics\cfg\models\11\yolo11_msfn.yaml")\
        .load(r'D:\bilibili\model\YOLO11\ultralytics-main\yolo11n.pt')  # build from YAML and transfer weights

    results = model.train(data=r'D:\bilibili\model\ultralytics-main\ultralytics\cfg\datasets\VOC_my.yaml',
                          epochs=100, imgsz=640, batch=8)