一、模型框架概述
YoloV5模型框架采用了改进版本的SPP(Spatial Pyramid Pooling)结构和PANet(Path Aggregation Network)模块，同时采用了Swish激活函数，Padding=Same卷积核和Focus分离卷积等技术。整个模型包括Backbone、Neck和Head三个部分，其中Backbone部分为CSPdarknet53卷积神经网络，Neck部分为PANet模块，Head部分为YOLOv5模型的检测头部分。
二、数据预处理
YoloV5模型的输入数据格式为416x416的RGB图像，输入图像会经过归一化而统一成0~1之间的数值，之后进行图像增强操作，主要包括随机缩放、随机裁剪、随机反转、色彩调整等操作，这些操作能够增加模型的鲁棒性，提高模型的泛化能力。
def pre_process(img, img_size):
img = letterbox(img, new_shape=img_size)[0]
img = img[:, :, ::-1].transpose(2, 0, 1).astype(np.float32)
img /= 255.0
return img
三、Backbone
YoloV5的Backbone部分采用了CSPdarknet53卷积神经网络结构。CSPdarknet53中的CSP(Block)模块是一种卷积神经网络结构，它采用了残差连接、通道分离和聚合(Bottleneck)的方式，融合了两条不同的特征传递路径，用于深度特征的提取和运算。使用这种方式可以避免特征信息损失，提高模型的拟合能力。
class CSP(nn.Module):
def __init__(self, in_channels, out_channels, n=1, shortcut=True, activation=True):
super(CSP, self).__init__()
self.shortcut = shortcut
self.activation = activation
self.layers = nn.Sequential()
self.layers.add_module("conv1", nn.Conv2d(in_channels, out_channels // 2, 1, 1, 0, bias=False))
self.layers.add_module("bn1", nn.BatchNorm2d(out_channels // 2))
self.layers.add_module("act1", nn.LeakyReLU(0.1))
for i in range(n):
self.layers.add_module("conv2_%d" % i, nn.Conv2d(out_channels // 2, out_channels // 2, 3, 1, 1, bias=False))
self.layers.add_module("bn2_%d" % i, nn.BatchNorm2d(out_channels // 2))
self.layers.add_module("act2_%d" % i, nn.LeakyReLU(0.1))
self.layers.add_module("conv3", nn.Conv2d(out_channels // 2, out_channels, 1, 1, 0, bias=False))
self.layers.add_module("bn3", nn.BatchNorm2d(out_channels))
if self.activation:
self.layers.add_module("act3", nn.LeakyReLU(0.1))
def forward(self, x):
if self.shortcut:
x1, x2 = x.chunk(2, dim=1)
out = self.layers(x2)
out = torch.cat([out, x1], dim=1)
else:
out = self.layers(x)
return out
四、Neck
YoloV5的Neck部分采用了PANet模块。PANet是一种基于特征金字塔(FPN)和聚合网络的模块，可用于网络特征表达的增强，实现多尺度特征的聚合与利用。PANet模块中采用了一个自上而下及自下而上的聚合过程，该过程能够解决不同尺度特征信息的传递问题，提高模型对小目标的检测率和准确率。
class PANet(nn.Module):
def __init__(self, channels):
super(PANet, self).__init__()
self.layers1 = nn.Sequential(
nn.Conv2d(channels[1], channels[1], kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(channels[1]),
nn.LeakyReLU(0.1),
nn.Conv2d(channels[1], channels[0], kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(channels[0]),
nn.LeakyReLU(0.1)
)
self.layers2 = nn.Sequential(
nn.Conv2d(channels[0], channels[0], kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(channels[0]),
nn.LeakyReLU(0.1),
nn.Conv2d(channels[0], channels[0], kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(channels[0]),
nn.LeakyReLU(0.1)
)
self.layers3 = nn.Sequential(
nn.Conv2d(channels[0], channels[0], kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(channels[0]),
nn.LeakyReLU(0.1),
nn.Conv2d(channels[0], channels[0], kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(channels[0]),
nn.LeakyReLU(0.1)
)
self.layers4 = nn.Sequential(
nn.Conv2d(channels[0], channels[0], kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(channels[0]),
nn.LeakyReLU(0.1),
nn.Conv2d(channels[0], channels[0], kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(channels[0]),
nn.LeakyReLU(0.1)
)
self.fuse = nn.Sequential(
nn.Conv2d(channels[0], channels[0], kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(channels[0]),
nn.LeakyReLU(0.1)
)
self.downsample = nn.MaxPool2d(kernel_size=2, stride=2)
def forward(self, x):
out = self.layers1(x[0])
x2 = self.fuse(torch.cat((out, F.upsample(x[1], scale_factor=2)), dim=1))
x3 = self.layers2(x2)
x4 = self.layers3(x3)
x5 = self.layers4(x4)
x5 = self.downsample(x5)
return x2, x3, x4, x5
五、Head
YoloV5的Head部分采用了YOLOv5模型的检测头部分。在YoloV5中，检测头主要包括Anchor定义、预测调整、处理NMS、处理特征图等操作。采用这种方式可以有效地提高模型的准确率和召回率，同时还能够保证目标检测的稳定性和一致性。
class Detection(nn.Module):
def __init__(self, in_channels, num_classes, anchors):
super(Detection, self).__init__()
self.num_anchors = len(anchors)
self.num_classes = num_classes
self.in_channels = in_channels
self.conv = nn.Conv2d(self.in_channels, self.num_anchors * (self.num_classes + 5), kernel_size=1, stride=1, padding=0)
self.init_conv2d()
def forward(self, x):
out = self.conv(x)
out = out.permute(0, 2, 3, 1)
return out.reshape(out.shape[0], -1, self.num_classes + 5)
def init_conv2d(self):
bias_value = -4.0
nn.init.normal_(self.conv.weight, std=0.01)
nn.init.constant_(self.conv.bias, bias_value)
六、总结
综上所述，YoloV5模型采用了CSPdarknet53卷积神经网络、PANet模块等技术，通过Backbone、Neck和Head三部分实现了高效、准确的目标检测。除此之外，模型还采用了图像增强、Swish激活函数、Padding=Same卷积核和Focus分离卷积等技术来提高模型的鲁棒性和泛化能力。
