二阶段目标检测网络-Mask RCNN 详解

Mask RCNN 是作者 Kaiming He 于 2018 年发表的论文。

ROI Pooling 和 ROI Align 的区别

Understanding Region of Interest — (RoI Align and RoI Warp)

Mask R-CNN 网络结构

Mask RCNN 继承自 Faster RCNN 主要有三个改进：

• feature map 的提取采用了 FPN 的多尺度特征网络
• ROI Pooling 改进为 ROI Align
• 在 RPN 后面，增加了采用 FCN 结构的 mask 分割分支

网络结构如下图所示：

可以看出，Mask RCNN 是一种先检测物体，再分割的思路，简单直接，在建模上也更有利于网络的学习。

骨干网络 FPN

卷积网络的一个重要特征：深层网络容易响应语义特征，浅层网络容易响应图像特征。Mask RCNN 的使用了 ResNet 和 FPN 结合的网络作为特征提取器。

FPN 的代码出现在 ./mrcnn/model.py中，核心代码如下：

if callable(config.BACKBONE):
    _, C2, C3, C4, C5 = config.BACKBONE(input_image, stage5=True,
                                        train_bn=config.TRAIN_BN)
else:
    _, C2, C3, C4, C5 = resnet_graph(input_image, config.BACKBONE,
                                        stage5=True, train_bn=config.TRAIN_BN)
# Top-down Layers
# TODO: add assert to varify feature map sizes match what's in config
P5 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c5p5')(C5)
P4 = KL.Add(name="fpn_p4add")([
    KL.UpSampling2D(size=(2, 2), name="fpn_p5upsampled")(P5),
    KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c4p4')(C4)])
P3 = KL.Add(name="fpn_p3add")([
    KL.UpSampling2D(size=(2, 2), name="fpn_p4upsampled")(P4),
    KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c3p3')(C3)])
P2 = KL.Add(name="fpn_p2add")([
    KL.UpSampling2D(size=(2, 2), name="fpn_p3upsampled")(P3),
    KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c2p2')(C2)])
# Attach 3x3 conv to all P layers to get the final feature maps.
P2 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p2")(P2)
P3 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p3")(P3)
P4 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p4")(P4)
P5 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p5")(P5)
# P6 is used for the 5th anchor scale in RPN. Generated by
# subsampling from P5 with stride of 2.
P6 = KL.MaxPooling2D(pool_size=(1, 1), strides=2, name="fpn_p6")(P5)

# Note that P6 is used in RPN, but not in the classifier heads.
rpn_feature_maps = [P2, P3, P4, P5, P6]
mrcnn_feature_maps = [P2, P3, P4, P5]

其中 resnet_graph 函数定义如下：

def resnet_graph(input_image, architecture, stage5=False, train_bn=True):
    """Build a ResNet graph.
        architecture: Can be resnet50 or resnet101
        stage5: Boolean. If False, stage5 of the network is not created
        train_bn: Boolean. Train or freeze Batch Norm layers
    """
    assert architecture in ["resnet50", "resnet101"]
    # Stage 1
    x = KL.ZeroPadding2D((3, 3))(input_image)
    x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x)
    x = BatchNorm(name='bn_conv1')(x, training=train_bn)
    x = KL.Activation('relu')(x)
    C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)
    # Stage 2
    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), train_bn=train_bn)
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', train_bn=train_bn)
    C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', train_bn=train_bn)
    # Stage 3
    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', train_bn=train_bn)
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', train_bn=train_bn)
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', train_bn=train_bn)
    C3 = x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', train_bn=train_bn)
    # Stage 4
    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', train_bn=train_bn)
    block_count = {"resnet50": 5, "resnet101": 22}[architecture]
    for i in range(block_count):
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block=chr(98 + i), train_bn=train_bn)
    C4 = x
    # Stage 5
    if stage5:
        x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', train_bn=train_bn)
        x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', train_bn=train_bn)
        C5 = x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', train_bn=train_bn)
    else:
        C5 = None
    return [C1, C2, C3, C4, C5]

anchor 锚框生成规则

在 Faster-RCNN 中可以将 SCALE 也可以设置为多个值，而在 Mask RCNN 中则是每一特征层只对应着一个SCALE 即对应着上述所设置的 16。

实验

何凯明在论文中做了很多对比单个模块试验，并放出了对比结果表格。

从上图表格可以看出：

• sigmoid 和 softmax 对比，sigmoid 有不小提升；
• 特征网络选择：可以看出更深的网络和采用 FPN 的实验效果更好，可能因为 FPN 综合考虑了不同尺寸的 feature map 的信息，因此能够把握一些更精细的细节。
• RoI Align 和 RoI Pooling 对比：在 instance segmentation 和 object detection 上都有不小的提升。这样看来，RoIAlign 其实就是一个更加精准的 RoIPooling，把前者放到 Faster RCNN 中，对结果的提升应该也会有帮助。

参考资料

Mask R-CNN 论文