目标检测——SSD模型搭建

SSD采用改造的VGG16加额外4个特征层，实现总体的特征提取，生成不同大小的feature_map

 

改造FC6 和FC7，采用卷积而不是全连接，生成19*19 的feature_map, 额外增加4个卷积层，生成10*10， 5*5， 3*3， 1*1 的feature_map，

其中conv4_3, FC7, conv6_2, conv7_2, conv8_2, conv9_2作为特征提取层，用于目标框的回归和类别的识别。

 

def SSD300(input_shape, num_classes=21):
    # 300,300,3
    input_tensor = Input(shape=input_shape)
    img_size = (input_shape[1], input_shape[0])

    # SSD结构,net字典
    net = VGG16(input_tensor)
    #-----------------------将提取到的主干特征进行处理---------------------------#
    # 对conv4_3进行处理 38,38,512
    net['conv4_3_norm'] = Normalize(20, name='conv4_3_norm')(net['conv4_3'])
    num_priors = 4
    # 预测框的处理
    # num_priors表示每个网格点先验框的数量，4是x,y,h,w的调整
    net['conv4_3_norm_mbox_loc'] = Conv2D(num_priors * 4, kernel_size=(3,3), padding='same', name='conv4_3_norm_mbox_loc')(net['conv4_3_norm'])
    net['conv4_3_norm_mbox_loc_flat'] = Flatten(name='conv4_3_norm_mbox_loc_flat')(net['conv4_3_norm_mbox_loc'])
    # num_priors表示每个网格点先验框的数量，num_classes是所分的类
    net['conv4_3_norm_mbox_conf'] = Conv2D(num_priors * num_classes, kernel_size=(3,3), padding='same',name='conv4_3_norm_mbox_conf')(net['conv4_3_norm'])
    net['conv4_3_norm_mbox_conf_flat'] = Flatten(name='conv4_3_norm_mbox_conf_flat')(net['conv4_3_norm_mbox_conf'])
    priorbox = PriorBox(img_size, 30.0,max_size = 60.0, aspect_ratios=[2],
                        variances=[0.1, 0.1, 0.2, 0.2],
                        name='conv4_3_norm_mbox_priorbox')
    net['conv4_3_norm_mbox_priorbox'] = priorbox(net['conv4_3_norm'])
    
    # 对fc7层进行处理 
    num_priors = 6
    # 预测框的处理
    # num_priors表示每个网格点先验框的数量，4是x,y,h,w的调整
    net['fc7_mbox_loc'] = Conv2D(num_priors * 4, kernel_size=(3,3),padding='same',name='fc7_mbox_loc')(net['fc7'])
    net['fc7_mbox_loc_flat'] = Flatten(name='fc7_mbox_loc_flat')(net['fc7_mbox_loc'])
    # num_priors表示每个网格点先验框的数量，num_classes是所分的类
    net['fc7_mbox_conf'] = Conv2D(num_priors * num_classes, kernel_size=(3,3),padding='same',name='fc7_mbox_conf')(net['fc7'])
    net['fc7_mbox_conf_flat'] = Flatten(name='fc7_mbox_conf_flat')(net['fc7_mbox_conf'])

    priorbox = PriorBox(img_size, 60.0, max_size=111.0, aspect_ratios=[2, 3],
                        variances=[0.1, 0.1, 0.2, 0.2],
                        name='fc7_mbox_priorbox')
    net['fc7_mbox_priorbox'] = priorbox(net['fc7'])

    # 对conv6_2进行处理
    num_priors = 6
    # 预测框的处理
    # num_priors表示每个网格点先验框的数量，4是x,y,h,w的调整
    x = Conv2D(num_priors * 4, kernel_size=(3,3), padding='same',name='conv6_2_mbox_loc')(net['conv6_2'])
    net['conv6_2_mbox_loc'] = x
    net['conv6_2_mbox_loc_flat'] = Flatten(name='conv6_2_mbox_loc_flat')(net['conv6_2_mbox_loc'])
    # num_priors表示每个网格点先验框的数量，num_classes是所分的类
    x = Conv2D(num_priors * num_classes, kernel_size=(3,3), padding='same',name='conv6_2_mbox_conf')(net['conv6_2'])
    net['conv6_2_mbox_conf'] = x
    net['conv6_2_mbox_conf_flat'] = Flatten(name='conv6_2_mbox_conf_flat')(net['conv6_2_mbox_conf'])

    priorbox = PriorBox(img_size, 111.0, max_size=162.0, aspect_ratios=[2, 3],
                        variances=[0.1, 0.1, 0.2, 0.2],
                        name='conv6_2_mbox_priorbox')
    net['conv6_2_mbox_priorbox'] = priorbox(net['conv6_2'])

    # 对conv7_2进行处理
    num_priors = 6
    # 预测框的处理
    # num_priors表示每个网格点先验框的数量，4是x,y,h,w的调整
    x = Conv2D(num_priors * 4, kernel_size=(3,3), padding='same',name='conv7_2_mbox_loc')(net['conv7_2'])
    net['conv7_2_mbox_loc'] = x
    net['conv7_2_mbox_loc_flat'] = Flatten(name='conv7_2_mbox_loc_flat')(net['conv7_2_mbox_loc'])
    # num_priors表示每个网格点先验框的数量，num_classes是所分的类
    x = Conv2D(num_priors * num_classes, kernel_size=(3,3), padding='same',name='conv7_2_mbox_conf')(net['conv7_2'])
    net['conv7_2_mbox_conf'] = x
    net['conv7_2_mbox_conf_flat'] = Flatten(name='conv7_2_mbox_conf_flat')(net['conv7_2_mbox_conf'])

    priorbox = PriorBox(img_size, 162.0, max_size=213.0, aspect_ratios=[2, 3],
                        variances=[0.1, 0.1, 0.2, 0.2],
                        name='conv7_2_mbox_priorbox')
    net['conv7_2_mbox_priorbox'] = priorbox(net['conv7_2'])

    # 对conv8_2进行处理
    num_priors = 4
    # 预测框的处理
    # num_priors表示每个网格点先验框的数量，4是x,y,h,w的调整
    x = Conv2D(num_priors * 4, kernel_size=(3,3), padding='same',name='conv8_2_mbox_loc')(net['conv8_2'])
    net['conv8_2_mbox_loc'] = x
    net['conv8_2_mbox_loc_flat'] = Flatten(name='conv8_2_mbox_loc_flat')(net['conv8_2_mbox_loc'])
    # num_priors表示每个网格点先验框的数量，num_classes是所分的类
    x = Conv2D(num_priors * num_classes, kernel_size=(3,3), padding='same',name='conv8_2_mbox_conf')(net['conv8_2'])
    net['conv8_2_mbox_conf'] = x
    net['conv8_2_mbox_conf_flat'] = Flatten(name='conv8_2_mbox_conf_flat')(net['conv8_2_mbox_conf'])

    priorbox = PriorBox(img_size, 213.0, max_size=264.0, aspect_ratios=[2],
                        variances=[0.1, 0.1, 0.2, 0.2],
                        name='conv8_2_mbox_priorbox')
    net['conv8_2_mbox_priorbox'] = priorbox(net['conv8_2'])

    # 对conv9_2进行处理
    num_priors = 4
    # 预测框的处理
    # num_priors表示每个网格点先验框的数量，4是x,y,h,w的调整
    x = Conv2D(num_priors * 4, kernel_size=(3,3), padding='same',name='conv9_2_mbox_loc')(net['conv9_2'])
    net['conv9_2_mbox_loc'] = x
    net['conv9_2_mbox_loc_flat'] = Flatten(name='conv9_2_mbox_loc_flat')(net['conv9_2_mbox_loc'])
    # num_priors表示每个网格点先验框的数量，num_classes是所分的类
    x = Conv2D(num_priors * num_classes, kernel_size=(3,3), padding='same',name='conv9_2_mbox_conf')(net['conv9_2'])
    net['conv9_2_mbox_conf'] = x
    net['conv9_2_mbox_conf_flat'] = Flatten(name='conv9_2_mbox_conf_flat')(net['conv9_2_mbox_conf'])
    
    priorbox = PriorBox(img_size, 264.0, max_size=315.0, aspect_ratios=[2],
                        variances=[0.1, 0.1, 0.2, 0.2],
                        name='conv9_2_mbox_priorbox')

    net['conv9_2_mbox_priorbox'] = priorbox(net['conv9_2'])

    # 将所有结果进行堆叠
    net['mbox_loc'] = Concatenate(axis=1, name='mbox_loc')([net['conv4_3_norm_mbox_loc_flat'],
                             net['fc7_mbox_loc_flat'],
                             net['conv6_2_mbox_loc_flat'],
                             net['conv7_2_mbox_loc_flat'],
                             net['conv8_2_mbox_loc_flat'],
                             net['conv9_2_mbox_loc_flat']])
                            
    net['mbox_conf'] = Concatenate(axis=1, name='mbox_conf')([net['conv4_3_norm_mbox_conf_flat'],
                              net['fc7_mbox_conf_flat'],
                              net['conv6_2_mbox_conf_flat'],
                              net['conv7_2_mbox_conf_flat'],
                              net['conv8_2_mbox_conf_flat'],
                              net['conv9_2_mbox_conf_flat']])
                             
    net['mbox_priorbox'] = Concatenate(axis=1, name='mbox_priorbox')([net['conv4_3_norm_mbox_priorbox'],
                                  net['fc7_mbox_priorbox'],
                                  net['conv6_2_mbox_priorbox'],
                                  net['conv7_2_mbox_priorbox'],
                                  net['conv8_2_mbox_priorbox'],
                                  net['conv9_2_mbox_priorbox']])
                                  
    # 8732,4
    net['mbox_loc'] = Reshape((-1, 4),name='mbox_loc_final')(net['mbox_loc'])
    # 8732,21
    net['mbox_conf'] = Reshape((-1, num_classes),name='mbox_conf_logits')(net['mbox_conf'])

　　# 转化为概率信息
    net['mbox_conf'] = Activation('softmax',name='mbox_conf_final')(net['mbox_conf'])

    net['predictions'] = Concatenate(axis=2, name='predictions')([net['mbox_loc'],
                               net['mbox_conf'],
                               net['mbox_priorbox']])
                               
    model = Model(net['input'], net['predictions'])
    return model

 

将各层输出的信息进行堆叠，维度为（None, 8732, 4+num_classes）

先验框信息也作为网络的输出，用于最后预测的时候根据对应的先验框信息回归

所以model 的输出维度为（None， 8732， 4+num_classes+8），最后一维的前四个代表数代表框回归偏差信息，后8个数代表先验框信息， 中间的数代表类别