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二阶段目标检测网络-Mask RCNN 详解 - 嵌入式视觉
source link: https://www.cnblogs.com/armcvai/p/16992202.html
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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 configP5 = 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。
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。
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