1.RFB-Net介绍

论文：https://arxiv.org/pdf/1711.07767.pdf

代码：GitHub - GOATmessi7/RFBNet: Receptive Field Block Net for Accurate and Fast Object Detection, ECCV 2018

受启发于人类视觉的Receptive Fields结构，本文提出RFB，将RFs的尺度、离心率纳入考虑范围，使用轻量级主干网也能提取到高判别性特征，使得检测器速度快、精度高；具体地，RFB基于RFs的不同尺度，使用不同的卷积核，设计了多分支的conv、pooling操作（makes use of multi-branch pooling with varying kernels），并通过虫洞卷积（dilated conv）来控制感受野的离心率，最后一步reshape操作后，形成生成的特征

RFs也已被深入研究，如Inception、ASPP、Deformable CNN：

RFB模块是一个多分支的卷积模块，它的内部结构被划分为两部分：

1.多分支卷积层：根据RF的定义，使用多种尺寸的卷积核来实现比固定尺寸更好。具体设计：1.瓶颈结构，1x1-s2的卷积减少通道特征，然后加上一个nxn卷积。2.用5x5卷积替换为2个3x3的卷积去减少参数，这样可得到非线性结构更好的层。3.为了输出，卷积经常有stride=2或者是减少通道，所有直连层为了匹配维度用一个不带激活函数的1x1卷积层。
2.dilated 卷积层：在保持参数量可扩大感受野，用来获取更高分辨率的特征。下图展示了两种RFB结构：RFB和RFB-s。每个分支都是一个正常卷积后面加一个dilated卷积，主要尺寸和dilated因子不同。（a）RFB整体上借鉴了Inception的思想，主要不同点在于引入了3个dilated卷积层。（b）RFB-s和RFB相比主要有两个改进，一方面用3x3的卷积层代替5x5卷积层，另一方面用1x3和3x1的卷积来代替3x3卷积，主要目的是为了减少计算量，类似Inception后期版本对Inception结构的改进。

实验结果

RFB模块：在table 2中，原始的SSD300实现了77.2%的mAP，通过简单的用RFB-max Pooling替代最后一个卷积层，我们将结果提升到了79.1%，获得了1.9%的提高，这表明了RFB模块的高效性。

2. RFB引入到yolov8

2.1修改modules.py

class BasicRFB(nn.Module):

    def __init__(self, in_planes, out_planes, stride=1, scale=0.1, map_reduce=8, vision=1, groups=1):
        super(BasicRFB, self).__init__()
        self.scale = scale
        self.out_channels = out_planes
        inter_planes = in_planes // map_reduce

        self.branch0 = nn.Sequential(
            BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),
            BasicConv(inter_planes, 2 * inter_planes, kernel_size=(3, 3), stride=stride, padding=(1, 1), groups=groups),
            BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 1,
                      dilation=vision + 1, relu=False, groups=groups)
        )
        self.branch1 = nn.Sequential(
            BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),
            BasicConv(inter_planes, 2 * inter_planes, kernel_size=(3, 3), stride=stride, padding=(1, 1), groups=groups),
            BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 2,
                      dilation=vision + 2, relu=False, groups=groups)
        )
        self.branch2 = nn.Sequential(
            BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),
            BasicConv(inter_planes, (inter_planes // 2) * 3, kernel_size=3, stride=1, padding=1, groups=groups),
            BasicConv((inter_planes // 2) * 3, 2 * inter_planes, kernel_size=3, stride=stride, padding=1,
                      groups=groups),
            BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 4,
                      dilation=vision + 4, relu=False, groups=groups)
        )

        self.ConvLinear = BasicConv(6 * inter_planes, out_planes, kernel_size=1, stride=1, relu=False)
        self.shortcut = BasicConv(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

2.2 修改tasks.py

from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify,
                                    Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus,BasicRFB)

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

 if m in (Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
                 BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x,BasicRFB)

2.3 yolov8_BasicRFB.yaml

# Ultralytics YOLO 🚀, GPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
nc: 1  # 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 GFLOPs
  s: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs
  m: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
  l: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
  x: [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, [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, BasicRFB, [256]]  # 16 

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 12], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 19 (P4/16-medium)
  - [-1, 1, BasicRFB, [512]]  # 20 

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 9], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 23 (P5/32-large)
  - [-1, 1, BasicRFB, [1024]]  # 24 

  - [[16, 20, 24], 1, Detect, [nc]]  # Detect(P3, P4, P5)