前言

在目标检测领域，大目标在图像中所占比例大，下采样不足容易缺少大目标的信息，其检测同样是一个具有挑战性的问题。YOLO系列算法以其高效快速的特点受到广泛关注，然而其基础模型在面对大目标时，仍存在一些局限性。本文将介绍如何在YOLOv11中添加大目标检测层，以提高对大目标的检测能力。

专栏目录：YOLOv11改进目录一览 | 涉及卷积层、轻量化、注意力、损失函数、Backbone、SPPF、Neck、检测头等全方位改进
专栏地址：YOLOv11改进专栏——以发表论文的角度，快速准确的找到有效涨点的创新点！

一、YOLOv11原始模型结构介绍

YOLOv11m原始模型结构如下：

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv10 object detection model. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
nc: 1 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
  # [depth, width, max_channels]
  m: [0.67, 0.75, 768]

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, SCDown, [512, 3, 2]] # 5-P4/16
  - [-1, 6, C2f, [512, True]]
  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
  - [-1, 3, C2fCIB, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]] # 9
  - [-1, 1, PSA, [1024]] # 10

# YOLOv10.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
  - [-1, 3, C2f, [512]] # 13

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 4], 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, C2fCIB, [512, True]] # 19 (P4/16-medium)

  - [-1, 1, SCDown, [512, 3, 2]]
  - [[-1, 10], 1, Concat, [1]] # cat head P5
  - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)

  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

二、有效特征层对应的检测头类别

2.1 P3/8 - small检测头

• 原始模型中的P3/8特征层对应的检测头主要用于检测相对较小的目标。其特征图大小相对较大，空间分辨率较高。
• 适合检测尺寸大概在8x8到16x16像素左右的目标。

  2.2 P4/16 - medium检测头

• 这个检测头对应的P4/16特征层经过了更多的下采样操作，相比P3/8特征图空间分辨率降低，但通道数增加，特征更抽象且有语义信息。
• 它主要用于检测中等大小的目标，尺寸范围大概在16x16到32x32像素左右。

  2.3 P5/32 - large检测头

• P5/32是经过最多下采样操作得到的特征层，其空间分辨率最低，但语义信息最强、全局感受野最大。
• 该检测头适合检测较大尺寸的目标，一般是尺寸在32x32像素以上的目标。

  2.4 新添加针对大目标的检测头

  • 新添加的检测头主要用于检测更大尺寸的目标。尺寸在64x64像素以上的超大目标。

💡这是因为在目标检测任务中，随着目标尺寸的增大，需要更能关注到整体轮廓的特征图来有效捕捉大目标特征。

三、添加大目标的检测层后的网络结构

# 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, [768, 3, 2]] # 7-P5/32
  - [-1, 2, C3k2, [768, True]]
  - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
  - [-1, 2, C3k2, [1024, True]]

  - [-1, 1, SPPF, [1024, 5]] # 11
  - [-1, 2, C2PSA, [1024]] # 12

# YOLO11n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 8], 1, Concat, [1]] # cat backbone P5
  - [-1, 2, C3k2, [768, False]] # 15

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, C3k2, [512, False]] # 18

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, C3k2, [256, False]] # 21 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 18], 1, Concat, [1]] # cat head P4
  - [-1, 2, C3k2, [512, False]] # 24 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 15], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [768, True]] # 27 (P5/32-large)

  - [-1, 1, Conv, [768, 3, 2]]
  - [[-1, 12], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # 30 (P6/64-xlarge)

  - [[21, 24, 27, 30], 1, Detect, [nc]] # Detect(P3, P4, P5)

四、实现代码及YOLOv11修改步骤

模块完整介绍、个人总结、实现代码、模块改进、二次创新以及各模型添加步骤参考如下地址：

https://blog.csdn.net/qq_42591591/article/details/143860087