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YOLOv8融合CPA-Enhancer【提高恶略天气的退化图像检测】

news 来源：原创 2025/4/25 10:22:37

1.CPA介绍

CPA-Enhancer通过链式思考提示机制实现了对未知退化条件下图像的自适应增强，显著提升了物体检测性能。其插件式设计便于集成到现有检测框架中，并在物体检测及其他视觉任务中设立了新的性能标准，展现了广泛的应用潜力。

关于CPA-Enhancer的详细介绍可以看论文：[2403.11220v3] CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object Detection under Unknown Degradations

Input Image│▼
[Conv + BN + ReLU]（通用预处理）│├─► 分支1：多尺度卷积提取├─► 分支2：亮度补偿模块├─► 分支3：注意力增强机制（通道注意力+空间注意力）└─► 分支4：上下文结构感知（Transformer 模块等）│▼
[特征融合 + 残差连接]│▼
Enhanced Feature Map → YOLOv8 Backbone

CPA 处理图像的核心机制

1. 多尺度特征提取（Multi-Scale Extraction）

使用多个不同尺寸的卷积核（如 1x1, 3x3, 5x5）或金字塔结构提取局部+全局特征；
有效增强图像纹理信息和边缘结构；
防止小目标被弱特征淹没。

2. 亮度与对比度增强（Light & Contrast Enhancement）

模仿传统图像增强方法（如 CLAHE、Gamma 校正等）思想，但使用神经网络完成：

- 网络自动学习一套光照补偿策略；
- 增强图像暗部细节；
- 保留高光部分结构。

3. 上下文感知（Context-Aware Attention）

利用注意力机制（如 SE、CBAM、Transformer-style Attention）增强重要区域；
学习图像中哪些部分应该被重点关注（如前景物体、边缘）；
抑制背景冗余信息。

4. 结构保留增强（Structure-Aware Enhancement）

保持图像结构（如边缘、角点）不被模糊化；
可能引入边缘检测或梯度引导模块，增强空间纹理信息；
可引入残差连接，减少特征漂移。

2.将CPA-Enhancer融合进YOLＯv8

2.1 步骤一：放代码

首先找到如下的目录'ultralytics/nn'，然后在这个目录下创建一个'Addmodules'文件夹，然后在这个目录下创建一个Enhancer.py文件，文件名字可以根据你自己的习惯起，然后将CPA-Enhancer的核心代码复制进去。

import torch
import torch.nn as nn
import torch.nn.functional as F
import numbers
from einops import rearrange
from einops.layers.torch import Rearrange__all__ = ['CPA_arch']class RFAConv(nn.Module):  # 基于Group Conv实现的RFAConvdef __init__(self, in_channel, out_channel, kernel_size=3, stride=1):super().__init__()self.kernel_size = kernel_sizeself.get_weight = nn.Sequential(nn.AvgPool2d(kernel_size=kernel_size, padding=kernel_size // 2, stride=stride),nn.Conv2d(in_channel, in_channel * (kernel_size ** 2), kernel_size=1,groups=in_channel, bias=False))self.generate_feature = nn.Sequential(nn.Conv2d(in_channel, in_channel * (kernel_size ** 2), kernel_size=kernel_size, padding=kernel_size // 2,stride=stride, groups=in_channel, bias=False),nn.BatchNorm2d(in_channel * (kernel_size ** 2)),nn.ReLU())self.conv = nn.Sequential(nn.Conv2d(in_channel, out_channel, kernel_size=kernel_size, stride=kernel_size),nn.BatchNorm2d(out_channel),nn.ReLU())def forward(self, x):b, c = x.shape[0:2]weight = self.get_weight(x)h, w = weight.shape[2:]weighted = weight.view(b, c, self.kernel_size ** 2, h, w).softmax(2)  # b c*kernel**2,h,w ->  b c k**2 h wfeature = self.generate_feature(x).view(b, c, self.kernel_size ** 2, h,w)  # b c*kernel**2,h,w ->  b c k**2 h w   获得感受野空间特征weighted_data = feature * weightedconv_data = rearrange(weighted_data, 'b c (n1 n2) h w -> b c (h n1) (w n2)', n1=self.kernel_size,# b c k**2 h w ->  b c h*k w*kn2=self.kernel_size)return self.conv(conv_data)class Downsample(nn.Module):def __init__(self, n_feat):super(Downsample, self).__init__()self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat // 2, kernel_size=3, stride=1, padding=1, bias=False),nn.PixelUnshuffle(2))def forward(self, x):return self.body(x)class Upsample(nn.Module):def __init__(self, n_feat):super(Upsample, self).__init__()self.body = nn.Sequential(nn.Conv2d(n_feat, n_feat * 2, kernel_size=3, stride=1, padding=1, bias=False),nn.PixelShuffle(2))def forward(self, x):  # (b,c,h,w)return self.body(x)  # (b,c/2,h*2,w*2)class SpatialAttention(nn.Module):def __init__(self):super(SpatialAttention, self).__init__()self.sa = nn.Conv2d(2, 1, 7, padding=3, padding_mode='reflect', bias=True)def forward(self, x):  # x:[b,c,h,w]x_avg = torch.mean(x, dim=1, keepdim=True)  # (b,1,h,w)x_max, _ = torch.max(x, dim=1, keepdim=True)  # (b,1,h,w)x2 = torch.concat([x_avg, x_max], dim=1)  # (b,2,h,w)sattn = self.sa(x2)  # 7x7conv (b,1,h,w)return sattn * xclass ChannelAttention(nn.Module):def __init__(self, dim, reduction=8):super(ChannelAttention, self).__init__()self.gap = nn.AdaptiveAvgPool2d(1)self.ca = nn.Sequential(nn.Conv2d(dim, dim // reduction, 1, padding=0, bias=True),nn.ReLU(inplace=True),  # Relunn.Conv2d(dim // reduction, dim, 1, padding=0, bias=True),)def forward(self, x):  # x:[b,c,h,w]x_gap = self.gap(x)  #  [b,c,1,1]cattn = self.ca(x_gap)  # [b,c,1,1]return cattn * xclass Channel_Shuffle(nn.Module):def __init__(self, num_groups):super(Channel_Shuffle, self).__init__()self.num_groups = num_groupsdef forward(self, x):batch_size, chs, h, w = x.shapechs_per_group = chs // self.num_groupsx = torch.reshape(x, (batch_size, self.num_groups, chs_per_group, h, w))# (batch_size, num_groups, chs_per_group, h, w)x = x.transpose(1, 2)  # dim_1 and dim_2out = torch.reshape(x, (batch_size, -1, h, w))return outclass TransformerBlock(nn.Module):def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type):super(TransformerBlock, self).__init__()self.norm1 = LayerNorm(dim, LayerNorm_type)self.attn = Attention(dim, num_heads, bias)self.norm2 = LayerNorm(dim, LayerNorm_type)self.ffn = FeedForward(dim, ffn_expansion_factor, bias)def forward(self, x):x = x + self.attn(self.norm1(x))x = x + self.ffn(self.norm2(x))return xdef to_3d(x):return rearrange(x, 'b c h w -> b (h w) c')def to_4d(x, h, w):return rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)class BiasFree_LayerNorm(nn.Module):def __init__(self, normalized_shape):super(BiasFree_LayerNorm, self).__init__()if isinstance(normalized_shape, numbers.Integral):normalized_shape = (normalized_shape,)normalized_shape = torch.Size(normalized_shape)assert len(normalized_shape) == 1self.weight = nn.Parameter(torch.ones(normalized_shape))self.normalized_shape = normalized_shapedef forward(self, x):sigma = x.var(-1, keepdim=True, unbiased=False)return x / torch.sqrt(sigma + 1e-5) * self.weightclass WithBias_LayerNorm(nn.Module):def __init__(self, normalized_shape):super(WithBias_LayerNorm, self).__init__()if isinstance(normalized_shape, numbers.Integral):normalized_shape = (normalized_shape,)normalized_shape = torch.Size(normalized_shape)assert len(normalized_shape) == 1self.weight = nn.Parameter(torch.ones(normalized_shape))self.bias = nn.Parameter(torch.zeros(normalized_shape))self.normalized_shape = normalized_shapedef forward(self, x):device = x.devicemu = x.mean(-1, keepdim=True)sigma = x.var(-1, keepdim=True, unbiased=False)result = (x - mu) / torch.sqrt(sigma + 1e-5) * self.weight.to(device) + self.bias.to(device)return resultclass LayerNorm(nn.Module):def __init__(self, dim, LayerNorm_type):super(LayerNorm, self).__init__()if LayerNorm_type == 'BiasFree':self.body = BiasFree_LayerNorm(dim)else:self.body = WithBias_LayerNorm(dim)def forward(self, x):h, w = x.shape[-2:]return to_4d(self.body(to_3d(x)), h, w)class FeedForward(nn.Module):def __init__(self, dim, ffn_expansion_factor, bias):super(FeedForward, self).__init__()hidden_features = int(dim * ffn_expansion_factor)self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias)self.dwconv = nn.Conv2d(hidden_features * 2, hidden_features * 2, kernel_size=3, stride=1, padding=1,groups=hidden_features * 2, bias=bias)self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)def forward(self, x):device = x.deviceself.project_in = self.project_in.to(device)self.dwconv = self.dwconv.to(device)self.project_out = self.project_out.to(device)x = self.project_in(x)x1, x2 = self.dwconv(x).chunk(2, dim=1)x = F.gelu(x1) * x2x = self.project_out(x)return xclass Attention(nn.Module):def __init__(self, dim, num_heads, bias):super(Attention, self).__init__()self.num_heads = num_headsself.temperature = nn.Parameter(torch.ones(num_heads, 1, 1, dtype=torch.float32), requires_grad=True)self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)self.qkv_dwconv = nn.Conv2d(dim * 3, dim * 3, kernel_size=3, stride=1, padding=1, groups=dim * 3,bias=bias)self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)def forward(self, x):b, c, h, w = x.shapedevice = x.deviceself.qkv = self.qkv.to(device)self.qkv_dwconv = self.qkv_dwconv.to(device)self.project_out = self.project_out.to(device)qkv = self.qkv(x)qkv = self.qkv_dwconv(qkv)q, k, v = qkv.chunk(3, dim=1)q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=self.num_heads)k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=self.num_heads)v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=self.num_heads)q = torch.nn.functional.normalize(q, dim=-1)k = torch.nn.functional.normalize(k, dim=-1)attn = (q @ k.transpose(-2, -1)) * self.temperature.to(device)attn = attn.softmax(dim=-1)out = (attn @ v)out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=self.num_heads, h=h, w=w)out = self.project_out(out)return outclass resblock(nn.Module):def __init__(self, dim):super(resblock, self).__init__()# self.norm = LayerNorm(dim, LayerNorm_type='BiasFree')self.body = nn.Sequential(nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, bias=False),nn.PReLU(),nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, bias=False))def forward(self, x):res = self.body((x))res += xreturn res#########################################################################
# Chain-of-Thought Prompt Generation Module (CGM)
class CotPromptParaGen(nn.Module):def __init__(self,prompt_inch,prompt_size, num_path=3):super(CotPromptParaGen, self).__init__()# (128,32,32)->(64,64,64)->(32,128,128)self.chain_prompts=nn.ModuleList([nn.ConvTranspose2d(in_channels=prompt_inch if idx==0 else prompt_inch//(2**idx),out_channels=prompt_inch//(2**(idx+1)),kernel_size=3, stride=2, padding=1) for idx in range(num_path)])def forward(self,x):prompt_params = []prompt_params.append(x)for pe in self.chain_prompts:x=pe(x)prompt_params.append(x)return prompt_params#########################################################################
# Content-driven Prompt Block (CPB)
class ContentDrivenPromptBlock(nn.Module):def __init__(self, dim, prompt_dim, reduction=8, num_splits=4):super(ContentDrivenPromptBlock, self).__init__()self.dim = dimself.num_splits = num_splitsself.pa2 = nn.Conv2d(2 * dim, dim, 7, padding=3, padding_mode='reflect', groups=dim, bias=True)self.sigmoid = nn.Sigmoid()self.conv3x3 = nn.Conv2d(prompt_dim, prompt_dim, kernel_size=3, stride=1, padding=1, bias=False)self.conv1x1 = nn.Conv2d(dim, prompt_dim, kernel_size=1, stride=1, bias=False)self.sa = SpatialAttention()self.ca = ChannelAttention(dim, reduction)self.myshuffle = Channel_Shuffle(2)self.out_conv1 = nn.Conv2d(prompt_dim + dim, dim, kernel_size=1, stride=1, bias=False)self.transformer_block = [TransformerBlock(dim=dim // num_splits, num_heads=1, ffn_expansion_factor=2.66, bias=False,LayerNorm_type='WithBias') for _ in range(num_splits)]def forward(self, x, prompt_param):# latent: (b,dim*8,h/8,w/8)  prompt_param3: (1, 256, 16, 16)x_ = xB, C, H, W = x.shapecattn = self.ca(x)  # channel-wise attnsattn = self.sa(x)  # spatial-wise attnpattn1 = sattn + cattnpattn1 = pattn1.unsqueeze(dim=2)  # [b,c,1,h,w]x = x.unsqueeze(dim=2)  # [b,c,1,h,w]x2 = torch.cat([x, pattn1], dim=2)  #  [b,c,2,h,w]x2 = Rearrange('b c t h w -> b (c t) h w')(x2)  # [b,c*2,h,w]x2 = self.myshuffle(x2)  # [c1,c1_att,c2,c2_att,...]pattn2 = self.pa2(x2)pattn2 = self.conv1x1(pattn2)  # [b,prompt_dim,h,w]prompt_weight = self.sigmoid(pattn2)  # Sigmodprompt_param = F.interpolate(prompt_param, (H, W), mode="bilinear")# (b,prompt_dim,prompt_size,prompt_size) -> (b,prompt_dim,h,w)prompt = prompt_weight * prompt_paramprompt = self.conv3x3(prompt)  # (b,prompt_dim,h,w)inter_x = torch.cat([x_, prompt], dim=1)  # (b,prompt_dim+dim,h,w)inter_x = self.out_conv1(inter_x)  # (b,dim,h,w) dim=64splits = torch.split(inter_x, self.dim // self.num_splits, dim=1)transformered_splits = []for i, split in enumerate(splits):transformered_split = self.transformer_block[i](split)transformered_splits.append(transformered_split)result = torch.cat(transformered_splits, dim=1)return result#########################################################################
# CPA_Enhancer
class CPA_arch(nn.Module):def __init__(self, c_in=3, c_out=3, dim=4, prompt_inch=128, prompt_size=32):super(CPA_arch, self).__init__()self.conv0 = RFAConv(c_in, dim)self.conv1 = RFAConv(dim, dim)self.conv2 = RFAConv(dim * 2, dim * 2)self.conv3 = RFAConv(dim * 4, dim * 4)self.conv4 = RFAConv(dim * 8, dim * 8)self.conv5 = RFAConv(dim * 8, dim * 4)self.conv6 = RFAConv(dim * 4, dim * 2)self.conv7 = RFAConv(dim * 2, c_out)self.down1 = Downsample(dim)self.down2 = Downsample(dim * 2)self.down3 = Downsample(dim * 4)self.prompt_param_ini = nn.Parameter(torch.rand(1, prompt_inch, prompt_size, prompt_size)) # (b,c,h,w)self.myPromptParamGen = CotPromptParaGen(prompt_inch=prompt_inch,prompt_size=prompt_size)self.prompt1 = ContentDrivenPromptBlock(dim=dim * 2 ** 1, prompt_dim=prompt_inch // 4, reduction=8)  # !!!!self.prompt2 = ContentDrivenPromptBlock(dim=dim * 2 ** 2, prompt_dim=prompt_inch // 2, reduction=8)self.prompt3 = ContentDrivenPromptBlock(dim=dim * 2 ** 3, prompt_dim=prompt_inch , reduction=8)self.up3 = Upsample(dim * 8)self.up2 = Upsample(dim * 4)self.up1 = Upsample(dim * 2)def forward(self, x):  # (b,c_in,h,w)prompt_params = self.myPromptParamGen(self.prompt_param_ini)prompt_param1 = prompt_params[2] # [1, 64, 64, 64]prompt_param2 = prompt_params[1]  # [1, 128, 32, 32]prompt_param3 = prompt_params[0]  # [1, 256, 16, 16]x0 = self.conv0(x)  # (b,dim,h,w)x1 = self.conv1(x0)  # (b,dim,h,w)x1_down = self.down1(x1)  # (b,dim,h/2,w/2)x2 = self.conv2(x1_down)  # (b,dim,h/2,w/2)x2_down = self.down2(x2)x3 = self.conv3(x2_down)x3_down = self.down3(x3)x4 = self.conv4(x3_down)device = x4.deviceself.prompt1 = self.prompt1.to(device)self.prompt2 = self.prompt2.to(device)self.prompt3 = self.prompt3.to(device)x4_prompt = self.prompt3(x4, prompt_param3)x3_up = self.up3(x4_prompt)x5 = self.conv5(torch.cat([x3_up, x3], 1))x5_prompt = self.prompt2(x5, prompt_param2)x2_up = self.up2(x5_prompt)x2_cat = torch.cat([x2_up, x2], 1)x6 = self.conv6(x2_cat)x6_prompt = self.prompt1(x6, prompt_param1)x1_up = self.up1(x6_prompt)x7 = self.conv7(torch.cat([x1_up, x1], 1))return x7if __name__ == "__main__":# Generating Sample imageimage_size = (1, 3, 640, 640)image = torch.rand(*image_size)out = CPA_arch(3, 3, 4)out = out(image)print(out.size())

2.2 步骤二：告诉 YOLOv8 有新东西

在Addmodules下创建一个新的py文件名字为'__init__.py',然后在其内部添加如下代码

2.3 步骤三：让 YOLOv8 认识新工具

在task.py进行导入

到此注册成功

2.4 步骤四：改 YOLOv8 的“说明书”

复制后面的yaml文件直接运行即可

yaml文件

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 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=yolov8n.yaml' will call yolov8.yaml with scale 'n'# [depth, width, max_channels]n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPss: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPsm: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPsl: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs# YOLOv8.0n backbone
backbone:# [from, repeats, module, args]- [-1, 1, CPA_arch, []]  # 0-P1/2- [-1, 1, Conv, [64, 3, 2]]  # 1-P1/2- [-1, 1, Conv, [128, 3, 2]]  # 2-P2/4- [-1, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]]  # 4-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]]  # 6-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [1024, 3, 2]]  # 8-P5/32- [-1, 3, C2f, [1024, True]]- [-1, 1, SPPF, [1024, 5]]  # 10# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 7], 1, Concat, [1]]  # cat backbone P4- [-1, 3, C2f, [512]]  # 13- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 5], 1, Concat, [1]]  # cat backbone P3- [-1, 3, C2f, [256]]  # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]]  # cat head P4- [-1, 3, C2f, [512]]  # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]]  # cat head P5- [-1, 3, C2f, [1024]]  # 22 (P5/32-large)- [[16, 19, 22], 1, Detect, [nc]]  # Detect(P3, P4, P5)