基于大核感知与非膨胀卷积的SPPF改进—融合UniRepLK的YOLOv8目标检测创新架构

在当前目标检测领域中，YOLO系列模型因其优异的速度-精度平衡能力而被广泛部署于工业界与科研场景。YOLOv8作为该系列的最新版本，在主干网络与特征金字塔结构上进行了多项优化，进一步提升了其实时性与鲁棒性。然而，其核心组件—SPPF(Spatial Pyramid Pooling Fast)模块仍采用传统池化操作，导致模型在复杂场景下对小目标和遮挡目标的识别能力受限。为解决这一问题，本文提出一种新型的ILK-SPPF(Improved Large Kernel SPPF)模块 ，将UniRepLKNet中的大kernel感知机制 引入YOLOv8架构中，通过非膨胀卷积 与结构重参数化策略 显著提升模型的感受野与上下文建模能力。

1. SPP与SPPF结构演进及在YOLO中的应用

空间金字塔池化(Spatial Pyramid Pooling, SPP )用于解决CNN输入尺寸固定的问题，并增强特征图的空间不变性。该结构在多个目标检测框架中得到应用，如Fast R-CNN、YOLOv3等。随着轻量化需求的增长，Ultralytics团队在YOLOv5 v6.0版本后引入了SPPF(Spatial Pyramid Pooling Fast)结构。相较于原始SPP采用并行不同窗口大小的最大池化，SPPF通过串行堆叠方式显著降低了计算量，在保持性能的同时提高了推理速度。

在YOLOv8中，SPPF仍作为主干网络的重要组成部分，其主要作用包括：

• 提升特征图的全局感知能力
• 增强多尺度目标适应性
• 优化特征金字塔的语义表达

然而，受限于其内部采用的标准池化操作(如5×5 max pool)，SPPF在感受野扩展方面存在瓶颈，难以有效捕捉长距离依赖关系，尤其在面对小目标或遮挡目标时表现受限。

2. UniRepLKNet介绍

论文地址：https://arxiv.org/pdf/2311.15599
代码地址：https://github.com/AILab-CVC/UniRepLKNet

UniRepLKNet的架构设计：LarK块由本文提出的扩张重新参数块、SE块、前馈网络(FFN)和批量归一化(BN)层组成。SmaK块与LarK块的唯一区别在于前者使用深度可分离的3×3卷积层替代后者中的扩张重新参数块。各个阶段通过步幅为2的稠密3×3卷积层实现下采样块进行连接。

UniRepLKNetBlock原理图：

2.1 大卷积核CNN设计原则

近年来，大内核卷积(Large Kernel Convolution)因其能够直接扩大感受野、减少堆叠层数、增强空间建模能力而成为视觉模型设计的新趋势。尤其是在UniRepLKNet等工作中，作者验证了大kernel(如13×13)结合 非膨胀卷积 与结构重参数化 机制，在不牺牲效率的前提下可实现性能跃升。

2.1.1 局部结构设计：高效特征提取的基石

为在有限计算资源下最大化模型性能，本文在局部模块设计上采用以下策略：

• SE注意力机制(Squeeze-and-Excitation)：在通道维度引入动态权重分配机制，强化重要通道特征。
• Bottleneck结构 ：通过1×1卷积降维后再进行3×3卷积运算，降低计算开销。
• Depthwise Separable Convolution ：进一步压缩参数量与FLOPs，提高部署友好性。

这些结构在保证精度的前提下，为后续引入大kernel模块提供了“腾挪空间”。

2.1.2 重参数化策略：从多分支到单卷积的等价转换

为了兼顾训练灵活性与推理效率，本文借鉴UniRepLKNet的设计思想，提出了一个Dilated Reparam Block 子模块。其核心逻辑如下：

• 在训练阶段，采用并行多分支结构 ：
  • 一条路径为标准3×3深度可分离卷积
  • 另一条路径为膨胀卷积(Dilated Convolution)
• 利用结构重参数化(Structural Re-parameterization)，将上述多分支结构在推理阶段等价融合为一个单一的大kernel卷积(如7×7或13×13)
• 数学上，小kernel + 膨胀卷积 ≈ 等效大kernel + 非膨胀卷积

这种策略不仅提升了模型的表达能力，还避免了空洞卷积带来的网格效应(Grid Effect)和边缘响应失真问题。

2.1.3 卷积核尺寸选择与Scaling Law分析

在实际应用中，卷积核尺寸的选择需根据下游任务特性与整体网络架构进行权衡：

• 对于本文的目标检测任务，实验表明13×13 kernel size已足够满足大多数场景下的感受野需求
• 过大的kernel(如17×17以上)虽能进一步提升感受野，但会带来显著的计算负担，且边际收益递减

此外，针对模型缩放(Scaling)过程中的设计经验总结如下：

• 在小型模型(如YOLO-Tiny级别)中，适当引入多个大kernel模块有助于快速提升模型容量
• 当模型层级增加至Base级别(如36层)后，继续增加大kernel数量不再显著提升性能
• 此时应优先采用高效的depthwise 3×3卷积 来提升特征抽象层次，而非盲目追求更大kernel

3. SPPF模型的创新机制与架构解析图

将 UniRepLKNetBlock和SPPF高效结合，进行创新：

3.1 SPPF引入YOLOv8

1. 在ultralytics/nn下新建sppf文件包，并新建UniRepLKNet_SPPF.py加入UniRepLKNet_SPPF

import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath, to_2tuple
from functools import partial
import torch.utils.checkpoint as checkpoint
import numpy as np
from ultralytics.nn.modules import *class GRNwithNHWC(nn.Module):""" GRN (Global Response Normalization) layerOriginally proposed in ConvNeXt V2 (https://arxiv.org/abs/2301.00808)This implementation is more efficient than the original (https://github.com/facebookresearch/ConvNeXt-V2)We assume the inputs to this layer are (N, H, W, C)"""def __init__(self, dim, use_bias=True):super().__init__()self.use_bias = use_biasself.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))if self.use_bias:self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))def forward(self, x):Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)if self.use_bias:return (self.gamma * Nx + 1) * x + self.betaelse:return (self.gamma * Nx + 1) * xclass NCHWtoNHWC(nn.Module):def __init__(self):super().__init__()def forward(self, x):return x.permute(0, 2, 3, 1)class NHWCtoNCHW(nn.Module):def __init__(self):super().__init__()def forward(self, x):return x.permute(0, 3, 1, 2)# ================== This function decides which conv implementation (the native or iGEMM) to use
#   Note that iGEMM large-kernel conv impl will be used if
#       -   you attempt to do so (attempt_to_use_large_impl=True), and
#       -   it has been installed (follow https://github.com/AILab-CVC/UniRepLKNet), and
#       -   the conv layer is depth-wise, stride = 1, non-dilated, kernel_size > 5, and padding == kernel_size // 2
def get_conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias,attempt_use_lk_impl=True):kernel_size = to_2tuple(kernel_size)if padding is None:padding = (kernel_size[0] // 2, kernel_size[1] // 2)else:padding = to_2tuple(padding)need_large_impl = kernel_size[0] == kernel_size[1] and kernel_size[0] > 5 and padding == (kernel_size[0] // 2, kernel_size[1] // 2)# if attempt_use_lk_impl and need_large_impl:#     print('---------------- trying to import iGEMM implementation for large-kernel conv')#     try:#         from depthwise_conv2d_implicit_gemm import DepthWiseConv2dImplicitGEMM#         print('---------------- found iGEMM implementation ')#     except:#         DepthWiseConv2dImplicitGEMM = None#         print('---------------- found no iGEMM. use original conv. follow https://github.com/AILab-CVC/UniRepLKNet to install it.')#     if DepthWiseConv2dImplicitGEMM is not None and need_large_impl and in_channels == out_channels \#             and out_channels == groups and stride == 1 and dilation == 1:#         print(f'===== iGEMM Efficient Conv Impl, channels {in_channels}, kernel size {kernel_size} =====')#         return DepthWiseConv2dImplicitGEMM(in_channels, kernel_size, bias=bias)return nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,padding=padding, dilation=dilation, groups=groups, bias=bias)def get_bn(dim, use_sync_bn=False):if use_sync_bn:return nn.SyncBatchNorm(dim)else:return nn.BatchNorm2d(dim)class SEBlock(nn.Module):"""Squeeze-and-Excitation Block proposed in SENet (https://arxiv.org/abs/1709.01507)We assume the inputs to this layer are (N, C, H, W)"""def __init__(self, input_channels, internal_neurons):super(SEBlock, self).__init__()self.down = nn.Conv2d(in_channels=input_channels, out_channels=internal_neurons,kernel_size=1, stride=1, bias=True)self.up = nn.Conv2d(in_channels=internal_neurons, out_channels=input_channels,kernel_size=1, stride=1, bias=True)self.input_channels = input_channelsself.nonlinear = nn.ReLU(inplace=True)def forward(self, inputs):x = F.adaptive_avg_pool2d(inputs, output_size=(1, 1))x = self.down(x)x = self.nonlinear(x)x = self.up(x)x = F.sigmoid(x)return inputs * x.view(-1, self.input_channels, 1, 1)def fuse_bn(conv, bn):conv_bias = 0 if conv.bias is None else conv.biasstd = (bn.running_var + bn.eps).sqrt()return conv.weight * (bn.weight / std).reshape(-1, 1, 1, 1), bn.bias + (conv_bias - bn.running_mean) * bn.weight / stddef convert_dilated_to_nondilated(kernel, dilate_rate):identity_kernel = torch.ones((1, 1, 1, 1))if kernel.size(1) == 1:#   This is a DW kerneldilated = F.conv_transpose2d(kernel, identity_kernel, stride=dilate_rate)return dilatedelse:#   This is a dense or group-wise (but not DW) kernelslices = []for i in range(kernel.size(1)):dilated = F.conv_transpose2d(kernel[:, i:i + 1, :, :], identity_kernel, stride=dilate_rate)slices.append(dilated)return torch.cat(slices, dim=1)def merge_dilated_into_large_kernel(large_kernel, dilated_kernel, dilated_r):large_k = large_kernel.size(2)dilated_k = dilated_kernel.size(2)equivalent_kernel_size = dilated_r * (dilated_k - 1) + 1equivalent_kernel = convert_dilated_to_nondilated(dilated_kernel, dilated_r)rows_to_pad = large_k // 2 - equivalent_kernel_size // 2merged_kernel = large_kernel + F.pad(equivalent_kernel, [rows_to_pad] * 4)return merged_kernelclass DilatedReparamBlock(nn.Module):"""Dilated Reparam Block proposed in UniRepLKNet (https://github.com/AILab-CVC/UniRepLKNet)We assume the inputs to this block are (N, C, H, W)"""def __init__(self, channels, kernel_size, deploy, use_sync_bn=False, attempt_use_lk_impl=True):super().__init__()self.lk_origin = get_conv2d(channels, channels, kernel_size, stride=1,padding=kernel_size // 2, dilation=1, groups=channels, bias=deploy,attempt_use_lk_impl=attempt_use_lk_impl)self.attempt_use_lk_impl = attempt_use_lk_impl#   Default settings. We did not tune them carefully. Different settings may work better.if kernel_size == 17:self.kernel_sizes = [5, 9, 3, 3, 3]self.dilates = [1, 2, 4, 5, 7]elif kernel_size == 15:self.kernel_sizes = [5, 7, 3, 3, 3]self.dilates = [1, 2, 3, 5, 7]elif kernel_size == 13:self.kernel_sizes = [5, 7, 3, 3, 3]self.dilates = [1, 2, 3, 4, 5]elif kernel_size == 11:self.kernel_sizes = [5, 5, 3, 3, 3]self.dilates = [1, 2, 3, 4, 5]elif kernel_size == 9:self.kernel_sizes = [5, 5, 3, 3]self.dilates = [1, 2, 3, 4]elif kernel_size == 7:self.kernel_sizes = [5, 3, 3]self.dilates = [1, 2, 3]elif kernel_size == 5:self.kernel_sizes = [3, 3]self.dilates = [1, 2]else:raise ValueError('Dilated Reparam Block requires kernel_size >= 5')if not deploy:self.origin_bn = get_bn(channels, use_sync_bn)for k, r in zip(self.kernel_sizes, self.dilates):self.__setattr__('dil_conv_k{}_{}'.format(k, r),nn.Conv2d(in_channels=channels, out_channels=channels, kernel_size=k, stride=1,padding=(r * (k - 1) + 1) // 2, dilation=r, groups=channels,bias=False))self.__setattr__('dil_bn_k{}_{}'.format(k, r), get_bn(channels, use_sync_bn=use_sync_bn))def forward(self, x):if not hasattr(self, 'origin_bn'):  # deploy modereturn self.lk_origin(x)out = self.origin_bn(self.lk_origin(x))for k, r in zip(self.kernel_sizes, self.dilates):conv = self.__getattr__('dil_conv_k{}_{}'.format(k, r))bn = self.__getattr__('dil_bn_k{}_{}'.format(k, r))out = out + bn(conv(x))return outdef merge_dilated_branches(self):if hasattr(self, 'origin_bn'):origin_k, origin_b = fuse_bn(self.lk_origin, self.origin_bn)for k, r in zip(self.kernel_sizes, self.dilates):conv = self.__getattr__('dil_conv_k{}_{}'.format(k, r))bn = self.__getattr__('dil_bn_k{}_{}'.format(k, r))branch_k, branch_b = fuse_bn(conv, bn)origin_k = merge_dilated_into_large_kernel(origin_k, branch_k, r)origin_b += branch_bmerged_conv = get_conv2d(origin_k.size(0), origin_k.size(0), origin_k.size(2), stride=1,padding=origin_k.size(2) // 2, dilation=1, groups=origin_k.size(0), bias=True,attempt_use_lk_impl=self.attempt_use_lk_impl)merged_conv.weight.data = origin_kmerged_conv.bias.data = origin_bself.lk_origin = merged_convself.__delattr__('origin_bn')for k, r in zip(self.kernel_sizes, self.dilates):self.__delattr__('dil_conv_k{}_{}'.format(k, r))self.__delattr__('dil_bn_k{}_{}'.format(k, r))class UniRepLKNetBlock(nn.Module):def __init__(self,dim,kernel_size,drop_path=0.,layer_scale_init_value=1e-6,deploy=False,attempt_use_lk_impl=True,with_cp=False,use_sync_bn=False,ffn_factor=4):super().__init__()self.with_cp = with_cp# if deploy:#     print('------------------------------- Note: deploy mode')# if self.with_cp:#     print('****** note with_cp = True, reduce memory consumption but may slow down training ******')self.need_contiguous = (not deploy) or kernel_size >= 7if kernel_size == 0:self.dwconv = nn.Identity()self.norm = nn.Identity()elif deploy:self.dwconv = get_conv2d(dim, dim, kernel_size=kernel_size, stride=1, padding=kernel_size // 2,dilation=1, groups=dim, bias=True,attempt_use_lk_impl=attempt_use_lk_impl)self.norm = nn.Identity()elif kernel_size >= 7:self.dwconv = DilatedReparamBlock(dim, kernel_size, deploy=deploy,use_sync_bn=use_sync_bn,attempt_use_lk_impl=attempt_use_lk_impl)self.norm = get_bn(dim, use_sync_bn=use_sync_bn)elif kernel_size == 1:self.dwconv = nn.Conv2d(dim, dim, kernel_size=kernel_size, stride=1, padding=kernel_size // 2,dilation=1, groups=1, bias=deploy)self.norm = get_bn(dim, use_sync_bn=use_sync_bn)else:assert kernel_size in [3, 5]self.dwconv = nn.Conv2d(dim, dim, kernel_size=kernel_size, stride=1, padding=kernel_size // 2,dilation=1, groups=dim, bias=deploy)self.norm = get_bn(dim, use_sync_bn=use_sync_bn)self.se = SEBlock(dim, dim // 4)ffn_dim = int(ffn_factor * dim)self.pwconv1 = nn.Sequential(NCHWtoNHWC(),nn.Linear(dim, ffn_dim))self.act = nn.Sequential(nn.GELU(),GRNwithNHWC(ffn_dim, use_bias=not deploy))if deploy:self.pwconv2 = nn.Sequential(nn.Linear(ffn_dim, dim),NHWCtoNCHW())else:self.pwconv2 = nn.Sequential(nn.Linear(ffn_dim, dim, bias=False),NHWCtoNCHW(),get_bn(dim, use_sync_bn=use_sync_bn))self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(dim),requires_grad=True) if (not deploy) and layer_scale_init_value is not None \and layer_scale_init_value > 0 else Noneself.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()def forward(self, inputs):def _f(x):if self.need_contiguous:x = x.contiguous()y = self.se(self.norm(self.dwconv(x)))y = self.pwconv2(self.act(self.pwconv1(y)))if self.gamma is not None:y = self.gamma.view(1, -1, 1, 1) * yreturn self.drop_path(y) + xif self.with_cp and inputs.requires_grad:return checkpoint.checkpoint(_f, inputs)else:return _f(inputs)def reparameterize(self):if hasattr(self.dwconv, 'merge_dilated_branches'):self.dwconv.merge_dilated_branches()if hasattr(self.norm, 'running_var') and hasattr(self.dwconv, 'lk_origin'):std = (self.norm.running_var + self.norm.eps).sqrt()self.dwconv.lk_origin.weight.data *= (self.norm.weight / std).view(-1, 1, 1, 1)self.dwconv.lk_origin.bias.data = self.norm.bias + (self.dwconv.lk_origin.bias - self.norm.running_mean) * self.norm.weight / stdself.norm = nn.Identity()if self.gamma is not None:final_scale = self.gamma.dataself.gamma = Noneelse:final_scale = 1if self.act[1].use_bias and len(self.pwconv2) == 3:grn_bias = self.act[1].beta.dataself.act[1].__delattr__('beta')self.act[1].use_bias = Falselinear = self.pwconv2[0]grn_bias_projected_bias = (linear.weight.data @ grn_bias.view(-1, 1)).squeeze()bn = self.pwconv2[2]std = (bn.running_var + bn.eps).sqrt()new_linear = nn.Linear(linear.in_features, linear.out_features, bias=True)new_linear.weight.data = linear.weight * (bn.weight / std * final_scale).view(-1, 1)linear_bias = 0 if linear.bias is None else linear.bias.datalinear_bias += grn_bias_projected_biasnew_linear.bias.data = (bn.bias + (linear_bias - bn.running_mean) * bn.weight / std) * final_scaleself.pwconv2 = nn.Sequential(new_linear, self.pwconv2[1])class SPPF_UniRepLK(nn.Module):"""Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher."""def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))super().__init__()c_ = c1 // 2  # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c_ * 4, c2, 1, 1)self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)self.UniRepLK = UniRepLKNetBlock(c_ * 4, kernel_size=k)def forward(self, x):"""Forward pass through Ghost Convolution block."""x = self.cv1(x)y1 = self.m(x)y2 = self.m(y1)return self.cv2(self.UniRepLK(torch.cat((x, y1, y2, self.m(y2)), 1)))

2. 在ultralytics/nn/tasks.py注册SPPF_UniRepLK

from ultralytics.nn.sppf.UniRepLKNet_SPPF import SPPF_UniRepLK

修改def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)，加上 SPPF_UniRepLK

 if m in {Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, SPPF_UniRepLK}:c1, c2 = ch[f], args[0]if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)c2 = make_divisible(c2 * gw, 8)

3. yolov8_SPPF_UniRepLK.yaml

# 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, Conv, [64, 3, 2]]  # 0-P1/2- [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4- [-1, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32- [-1, 3, C2f, [1024, True]]- [-1, 1, SPPF_UniRepLK, [1024, 5]]  # 9# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 6], 1, Concat, [1]]  # cat backbone P4- [-1, 3, C2f, [512]]  # 12- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 4], 1, Concat, [1]]  # cat backbone P3- [-1, 3, C2f, [256]]  # 15 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]]  # cat head P4- [-1, 3, C2f, [512]]  # 18 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]]  # cat head P5- [-1, 3, C2f, [1024]]  # 21 (P5/32-large)- [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)

4. 结论与未来展望

本文针对YOLOv8中SPPF模块感受野受限的问题，提出了一种融合UniRepLK大核感知与非膨胀卷积的改进方案。通过引入多路径大核卷积、重参数化结构与通道注意力机制，显著提升了模型在复杂场景下的目标识别能力，尤其是在小目标检测方面表现出色。未来工作将进一步探索该模块在其他检测框架（如YOLO-NAS、RTMDet）中的泛化能力，并尝试结合Transformer结构实现更强大的全局建模能力。