改进YOLOv7系列： 最新结合用于小目标的新CNN卷积构建块

• 统一使用 YOLOv7 代码框架，结合不同模块来构建不同的YOLO目标检测模型。
• 本项目包含大量的改进方式,降低改进难度,改进点包含【Backbone特征主干】、【Neck特征融合】、【Head检测头】、【注意力机制】、【IoU损失函数】、【NMS】、【Loss计算方式】、【自注意力机制】、【数据增强部分】、【标签分配策略】、【激活函数】等各个部分

在这篇文章中，将SPD结构加入到 YOLOv7结构中

一、SPD论文理论部分

卷积神经网络 (CNN) 在许多计算机视觉任务（例如图像分类和对象检测）中取得了巨大成功。然而，它们的性能在图像分辨率低或物体很小的更艰巨的任务中迅速下降。在本文中，我们指出，这源于现有 CNN 架构中存在缺陷但常见的设计，即使用跨步卷积和/或池化层，这会导致细粒度信息的丢失和对不太有效的特征表示的学习. 为此，我们提出了一个名为SPD-Conv的新 CNN 构建块来代替每个跨步卷积层和每个池化层（因此完全消除了它们）。SPD-Conv 由空间到深度(SPD) 层后跟非跨步卷积 (Conv) 层，可以应用于大多数（如果不是全部）CNN 架构。我们在两个最具代表性的计算机视觉任务下解释了这种新设计：对象检测和图像分类。然后，我们通过将 SPD-Conv 应用于 YOLOv5 和 ResNet 来创建新的 CNN 架构，并通过经验证明我们的方法明显优于最先进的深度学习模型，尤其是在具有低分辨率图像和小物体的更艰巨任务上。

网络架构

YOLO 是一系列非常流行的目标检测模型，其中我们选择了最新的 YOLOv5 [14]来演示。YOLOv5 使用 CSPDarknet53 [4]带有 SPP [12]模块作为其主干，PANet [23]作为它的脖子，和 YOLOv3 的头部 [26]作为其检测头。此外，它还使用了来自 YOLOv4 的各种数据增强方法和一些模块 [4]用于性能优化。它使用带有 sigmoid 层的交叉熵损失来计算对象性和分类损失，以及 CIoU 损失函数 [38]用于定位损失。CIoU 损失比 IoU 损失考虑更多细节，例如边缘重叠、中心距离和宽高比。

模块结构

二、将其应用到YOLOv7中

YOLOv7网络配置文件

增加以下yolov7_spd.yaml文件
代码演示

# parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple

# anchors
anchors:
  - [12,16, 19,36, 40,28]  # P3/8
  - [36,75, 76,55, 72,146]  # P4/16
  - [142,110, 192,243, 459,401]  # P5/32

# yolov7 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [32, 3, 1]],  # 0
  
   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2      
   [-1, 1, Conv, [64, 3, 1]],
   
   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4  
   [-1, 1, Conv, [64, 1, 1]],
   [-2, 1, Conv, [64, 1, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [[-1, -3, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1]],  # 11
         
   [-1, 1, MP, []],
   [-1, 1, Conv, [128, 1, 1]],
   [-3, 1, Conv, [128, 1, 1]],
   [-1, 1, Conv, [128, 3, 2]],
   [[-1, -3], 1, Concat, [1]],  # 16-P3/8  
   [-1, 1, Conv, [128, 1, 1]],
   [-2, 1, Conv, [128, 1, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [[-1, -3, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [512, 1, 1]],  # 24
         
   [-1, 1, MP, []],
   [-1, 1, Conv, [256, 1, 1]],
   [-3, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [256, 3, 2]],
   [[-1, -3], 1, Concat, [1]],  # 29-P4/16  
   [-1, 1, Conv, [256, 1, 1]],
   [-2, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [[-1, -3, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [1024, 1, 1]],  # 37
         
   [-1, 1, MP, []],
   [-1, 1, Conv, [512, 1, 1]],
   [-3, 1, Conv, [512, 1, 1]],
   [-1, 1, Conv, [512, 3, 2]],
   [[-1, -3], 1, Concat, [1]],  # 42-P5/32  
   [-1, 1, Conv, [256, 1, 1]],
   [-2, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [[-1, -3, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [1024, 1, 1]],  # 50
  ]

# yolov7 head
head:
  [[-1, 1, SPPCSPC, [512]], # 51
  
   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [37, 1, Conv, [256, 1, 1]], # route backbone P4
   [[-1, -2], 1, Concat, [1]],
   
   [-1, 1, Conv, [256, 1, 1]],
   [-2, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1]], # 63
   
   [-1, 1, Conv, [128, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [24, 1, Conv, [128, 1, 1]], # route backbone P3
   [[-1, -2], 1, Concat, [1]],
   
   [-1, 1, Conv, [128, 1, 1]],
   [-2, 1, Conv, [128, 1, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [128, 1, 1]], # 75
      
   [-1, 1, MP, []],
   [-1, 1, Conv, [128, 1, 1]],
   [-3, 1, Conv, [128, 1, 1]],
   [-1, 1, Conv, [128, 3, 2]],
   [[-1, -3, 63], 1, Concat, [1]],
   
   [-1, 1, Conv, [256, 1, 1]],
   [-2, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1]], # 88
      
   [-1, 1, MP, []],
   [-1, 1, Conv, [256, 1, 1]],
   [-3, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [256, 3, 2]],
   [[-1, -3, 51], 1, Concat, [1]],
   
   [-1, 1, Conv, [512, 1, 1]],
   [-2, 1, Conv, [512, 1, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [512, 1, 1]], # 101
   [-1,1,space_to_depth,[1]],   # 2 -P2/4
   [-1, 1, Conv, [512, 1, 1]], # 103
   
   [75, 1, RepConv, [256, 3, 1]],
   [88, 1, RepConv, [512, 3, 1]],
   [103, 1, RepConv, [1024, 3, 1]],

   [[104,105,106], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)
  ]

核心代码

./models/common.py文件增加以下模块

class space_to_depth(nn.Module):
    # Changing the dimension of the Tensor
    def __init__(self, dimension=1):
        super().__init__()
        self.d = dimension

    def forward(self, x):
         return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)

其他配置

不需要

三、YOLOv5配置

YOLOv5添加SPD.yaml配置文件

增加以下yolov5_spd.yaml文件

# Parameters
nc: 80  # number of classes
depth_multiple: 1  # model depth multiple
width_multiple: 1  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Focus, [64, 3]],     # 0-P1/2
   [-1, 1, Conv, [128, 3, 1]],  # 1
   [-1,1,space_to_depth,[1]],   # 2 -P2/4
   [-1, 3, C3, [128]],          # 3
   [-1, 1, Conv, [256, 3, 1]],  # 4
   [-1,1,space_to_depth,[1]],   # 5 -P3/8
   [-1, 6, C3, [256]],          # 6
   [-1, 1, Conv, [512, 3, 1]],  # 7-P4/16
   [-1,1,space_to_depth,[1]],   # 8 -P4/16
   [-1, 9, C3, [512]],          # 9
   [-1, 1, Conv, [1024, 3, 1]], # 10-P5/32
   [-1,1,space_to_depth,[1]],   # 11 -P5/32
   [-1, 3, C3, [1024]],         # 12
   [-1, 1, SPPF, [1024, 5]],    # 13
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],                    # 14
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],    # 15
   [[-1, 9], 1, Concat, [1]],                     # 16 cat backbone P4
   [-1, 3, C3, [512, False]],                     # 17

   [-1, 1, Conv, [256, 1, 1]],                    # 18
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],    # 19
   [[-1, 6], 1, Concat, [1]],                     # 20 cat backbone P3
   [-1, 3, C3, [256, False]],                     # 21 (P3/8-small)

   [-1, 1, Conv, [256, 3, 1]],                    # 22
   [-1,1,space_to_depth,[1]],                     # 23 -P2/4
   [[-1, 18], 1, Concat, [1]],                    # 24 cat head P4
   [-1, 3, C3, [512, False]],                     # 25 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 1]],                    # 26
   [-1,1,space_to_depth,[1]],                     # 27 -P2/4
   [[-1, 14], 1, Concat, [1]],                    # 28 cat head P5
   [-1, 3, C3, [1024, False]],                    # 29 (P5/32-large)

   [[21, 25, 29], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

核心代码

./models/common.py文件增加以下模块

class space_to_depth(nn.Module):
    # Changing the dimension of the Tensor
    def __init__(self, dimension=1):
        super().__init__()
        self.d = dimension

    def forward(self, x):
         return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)