目标检测论文：FoveaBox: Beyond Anchor-based Object Detector及其PyTorch实现

FoveaBox: Beyond Anchor-based Object DetectorPDF: https://arxiv.org/pdf/1904.03797v1.pdfPyTorch代码: https://github.com/shanglianlm0525/ObjectDetection-networkPyTorch代码:

mingo_敏

745人浏览 · 2019-10-06 08:49:31

mingo_敏 · 2019-10-06 08:49:31 发布

FoveaBox: Beyond Anchor-based Object Detector
PDF: https://arxiv.org/pdf/1904.03797v1.pdf
PyTorch代码: https://github.com/shanglianlm0525/PyTorch-Networks

创新点:
1 提出了fovea的概念,重新定义正负样本,正样本比GT更小,负样本更远离GT.
2 FPN结构不同层负责不同尺度的目标.
3 Anchor-Free

1 FoveaBox网络结构:

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2 方法介绍

2-1 尺度划分（Scale Assignment）

FPN输出5个层P3, P4, P5, P6, P7,每个层分别负责尺度为
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的,目标,其中 $S_{l}$ 分别为为 $32^{2}$ , $64^{2}$ , $128^{2}$ , $256^{2}$ , $512^{2}$ . $η$ 将控制每个层预测的目标尺度.如果 $\leq \sqrt{2}$ , 每层目标尺度不重叠,如果 $η>\sqrt{2}$ ,不同层预测的目标将会重叠,即同一个目标将会被多个层预测.

2-2 Fovea定义（Object Fovea）

因为目标真实的边框上的点通常远离目标中心,和背景较为接近,因此FoveaBox先计算出目标中心,然后对目标的真实边框向目标中心收缩一点,作为正样本, 同时向外扩展一点,作为负样本,中间的点忽略掉.

positive area (fovea)定义为:
在这里插入图片描述
其中
正负样本的划分为σ1 = 0:3;和 σ2 = 0:4

2-3 预测边框（Box Prediction）

FoveaBox 回归每一个 cell 的坐标映射回原始图像之后和对应的 ground truth 的偏移量.
在这里插入图片描述

3 实验结果

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PyTorch代码:

import torch
import torch.nn as nn
import torchvision

def Conv3x3ReLU(in_channels,out_channels):
    return nn.Sequential(
        nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size=3,stride=1,padding=1),
        nn.ReLU6(inplace=True)
    )

def locLayer(in_channels,out_channels):
    return nn.Sequential(
            Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
            Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
            Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
            Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1),
        )

def confLayer(in_channels,out_channels):
    return nn.Sequential(
        Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
        Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
        Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
        Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1),
    )

class FoveaBox(nn.Module):
    def __init__(self, num_classes=80):
        super(FoveaBox, self).__init__()
        self.num_classes = num_classes
        resnet = torchvision.models.resnet50()
        layers = list(resnet.children())

        self.layer1 = nn.Sequential(*layers[:5])
        self.layer2 = nn.Sequential(*layers[5])
        self.layer3 = nn.Sequential(*layers[6])
        self.layer4 = nn.Sequential(*layers[7])

        self.lateral5 = nn.Conv2d(in_channels=2048, out_channels=256, kernel_size=1)
        self.lateral4 = nn.Conv2d(in_channels=1024, out_channels=256, kernel_size=1)
        self.lateral3 = nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1)

        self.upsample4 = nn.ConvTranspose2d(in_channels=256, out_channels=256, kernel_size=4, stride=2, padding=1)
        self.upsample3 = nn.ConvTranspose2d(in_channels=256, out_channels=256, kernel_size=4, stride=2, padding=1)

        self.downsample6 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=2, padding=1)
        self.downsample6_relu = nn.ReLU6(inplace=True)
        self.downsample5 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=2, padding=1)

        self.loc_layer3 = locLayer(in_channels=256,out_channels=4)
        self.conf_layer3 = confLayer(in_channels=256,out_channels=self.num_classes)

        self.loc_layer4 = locLayer(in_channels=256, out_channels=4)
        self.conf_layer4 = confLayer(in_channels=256, out_channels=self.num_classes)

        self.loc_layer5 = locLayer(in_channels=256, out_channels=4)
        self.conf_layer5 = confLayer(in_channels=256, out_channels=self.num_classes)

        self.loc_layer6 = locLayer(in_channels=256, out_channels=4)
        self.conf_layer6 = confLayer(in_channels=256, out_channels=self.num_classes)

        self.loc_layer7 = locLayer(in_channels=256, out_channels=4)
        self.conf_layer7 = confLayer(in_channels=256, out_channels=self.num_classes)

        self.init_params()

    def init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        x = self.layer1(x)
        c3 =x = self.layer2(x)
        c4 =x = self.layer3(x)
        c5 = x = self.layer4(x)

        p5 = self.lateral5(c5)
        p4 = self.upsample4(p5) + self.lateral4(c4)
        p3 = self.upsample3(p4) + self.lateral3(c3)

        p6 = self.downsample5(p5)
        p7 = self.downsample6_relu(self.downsample6(p6))

        loc3 = self.loc_layer3(p3)
        conf3 = self.conf_layer3(p3)

        loc4 = self.loc_layer4(p4)
        conf4 = self.conf_layer4(p4)

        loc5 = self.loc_layer5(p5)
        conf5 = self.conf_layer5(p5)

        loc6 = self.loc_layer6(p6)
        conf6 = self.conf_layer6(p6)

        loc7 = self.loc_layer7(p7)
        conf7 = self.conf_layer7(p7)

        locs = torch.cat([loc3.permute(0, 2, 3, 1).contiguous().view(loc3.size(0), -1),
                    loc4.permute(0, 2, 3, 1).contiguous().view(loc4.size(0), -1),
                    loc5.permute(0, 2, 3, 1).contiguous().view(loc5.size(0), -1),
                    loc6.permute(0, 2, 3, 1).contiguous().view(loc6.size(0), -1),
                    loc7.permute(0, 2, 3, 1).contiguous().view(loc7.size(0), -1)],dim=1)

        confs = torch.cat([conf3.permute(0, 2, 3, 1).contiguous().view(conf3.size(0), -1),
                           conf4.permute(0, 2, 3, 1).contiguous().view(conf4.size(0), -1),
                           conf5.permute(0, 2, 3, 1).contiguous().view(conf5.size(0), -1),
                           conf6.permute(0, 2, 3, 1).contiguous().view(conf6.size(0), -1),
                           conf7.permute(0, 2, 3, 1).contiguous().view(conf7.size(0), -1),], dim=1)

        out = (locs, confs)
        return out

if __name__ == '__main__':
    model = FoveaBox()
    print(model)

    input = torch.randn(1, 3, 800, 800)
    out = model(input)
    print(out[0].shape)
    print(out[1].shape)