The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

最新推荐文章于 2021-10-19 12:40:59 发布

原创最新推荐文章于 2021-10-19 12:40:59 发布 · 232 阅读

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结合FCN和DenseNets的图像分割方法，利用DenseNets的密集连接特性进行特征重用，通过FCN实现上采样，有效提升图像分割精度。

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论文地址：http://xueshu.baidu.com/usercenter/paper/show?paperid=a76088e42441c28b0fcfc5ae45e04a2d&site=xueshu_se
FCN和DenseNets两个网络结合，在分类网络DenseNets进行上采样，实现图像分割.
FCN网络
在这里插入图片描述
FCN-Densenet

程序：

with tf.variable_scope(scope, preset_model, [inputs]) as sc:
    stack = slim.conv2d(inputs, n_filters_first_conv, [3, 3], scope='first_conv', activation_fn=None)   ##第一层，输出是(?,?,?,48)
    n_filters = n_filters_first_conv
    #####################
    # Downsampling path #
    #####################
    skip_connection_list = []
    for i in range(n_pool):
        # Dense Block
        stack, _ = DenseBlock(stack, n_layers_per_block[i], growth_rate, dropout_p, scope='denseblock%d' % (i+1))
        n_filters += growth_rate * n_layers_per_block[i]
        # At the end of the dense block, the current stack is stored in the skip_connections list
        skip_connection_list.append(stack)
        # Transition Down
        stack = TransitionDown(stack, n_filters, dropout_p, scope='transitiondown%d'%(i+1))
    skip_connection_list = skip_connection_list[::-1]
    #####################
    #     Bottleneck    #
    #####################
    # Dense Block
    # We will only upsample the new feature maps
    stack, block_to_upsample = DenseBlock(stack, n_layers_per_block[n_pool], growth_rate, dropout_p, scope='denseblock%d' % (n_pool + 1))
    #######################
    #   Upsampling path   #
    #######################
    for i in range(n_pool):
        # Transition Up ( Upsampling + concatenation with the skip connection)
        n_filters_keep = growth_rate * n_layers_per_block[n_pool + i]
        stack = TransitionUp(block_to_upsample, skip_connection_list[i], n_filters_keep,
                             scope='transitionup%d' % (n_pool + i + 1))  ###336,288,240,192,144
        # Dense Block
        # We will only upsample the new feature maps
        stack, block_to_upsample = DenseBlock(stack, n_layers_per_block[n_pool + i + 1], growth_rate, dropout_p, scope='denseblock%d' % (n_pool + i + 2))
    #####################
    #      Softmax      #
    #####################
    net = slim.conv2d(stack, num_classes, [1, 1], activation_fn=None, scope='logits')
net_output = tf.placeholder(tf.float32,shape=[None,None,None,num_classes])    #标签是4维的(批次，图片高，宽，类别)，就是每个像素点对应的类别
network, init_fn = model_builder.build_model(model_name=args.model, frontend=args.frontend, net_input=net_input,num_classes=num_classes,
                                             crop_width=args.crop_width, crop_height=args.crop_height, is_training=True)

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=network, labels=net_output))

opt = tf.train.RMSPropOptimizer(learning_rate=0.0001, decay=0.995).minimize(loss, var_list=[var for var in tf.trainable_variables()])

参考： https://github.com/GeorgeSeif/Semantic-Segmentation-Suite