V2AS问路
[object_detect]使用MobileNetSSD进行对象检测
阅读原文时间：2023年07月11日阅读：2

1.单帧图片识别

  • object_detection.py

    导入必要的包

    import numpy as np
    import argparse
    import cv2

    构造参数 parse 并解析参数

    ap = argparse.ArgumentParser()
    ap.add_argument("-i", "--image", required=True,
    help="path to input image")
    ap.add_argument("-p", "--prototxt", required=True,
    help="path to Caffe 'deploy' prototxt file")
    ap.add_argument("-m", "--model", required=True,
    help="path to Caffe pre-trained model")
    ap.add_argument("-c", "--confidence", type=float, default=0.2,
    help="minimum probability to filter weak detections")
    args = vars(ap.parse_args())

    初始化 MobileNet SSD 训练的类标签列表

    检测，然后为每个类生成一组边界框颜色

    CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
    "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
    "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
    "sofa", "train", "tvmonitor"]
    COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

    从磁盘加载我们的序列化模型

    print("[INFO] loading model…")
    net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

    加载输入图像并为图像构造一个输入 blob

    将大小调整为固定的 300x300 像素，然后对其进行标准化

    #（注意：标准化是通过 MobileNet SSD 完成执行的
    image = cv2.imread(args["image"])
    (h, w) = image.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)

    通过网络传递blob并获得检测

    预测

    print("[INFO] computing object detections…")
    net.setInput(blob)
    detections = net.forward()

    循环检测

    for i in np.arange(0, detections.shape[2]):
    # 提取与相关的置信度（即概率）
    confidence = detections[0, 0, i, 2]

    # 通过确保置信度大于最小置信度来过滤无效检测
if confidence > args["confidence"]:
    # 从类标签detections中提取索引，
    # 然后计算物体边界框的 (x, y) 坐标
    idx = int(detections[0, 0, i, 1])
    box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
    (startX, startY, endX, endY) = box.astype("int")
    # 显示预测结果
label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
print("[INFO] {}".format(label))
cv2.rectangle(image, (startX, startY), (endX, endY),
    COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(image, label, (startX, y),
    cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)</code></pre>
    显示输出图像
    

    cv2.imshow("Output", image)
    
cv2.waitKey(0)

  • 调用方法:

    用法

    python object_detection.py --image images/example_01.jpg --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel

  • 测试效果

2.视频流实时检测对象

  • real_time_object_detection.py

    导入必要的包

    from imutils.video import VideoStream
    from imutils.video import FPS
    import numpy as np
    import argparse
    import imutils
    import time
    import cv2

    构造参数 parse 并解析参数

    ap = argparse.ArgumentParser()
    ap.add_argument("-p", "--prototxt", required=True,
    help="path to Caffe 'deploy' prototxt file")
    ap.add_argument("-m", "--model", required=True,
    help="path to Caffe pre-trained model")
    ap.add_argument("-c", "--confidence", type=float, default=0.2,
    help="minimum probability to filter weak detections")
    args = vars(ap.parse_args())

    初始化 MobileNet SSD 训练的类标签列表

    检测，然后为每个类生成一组边界框颜色

    CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
    "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
    "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
    "sofa", "train", "tvmonitor"]
    COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

    从磁盘加载我们的序列化模型

    print("[INFO] loading model…")
    net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

    初始化视频流，允许摄像机传感器预加载，

    并初始化 FPS 计数器

    print("[INFO] starting video stream…")
    vs = VideoStream(src=0).start()
    time.sleep(2.0)
    fps = FPS().start()

    循环读取视频流中的帧

    while True:
    # 从线程视频流中抓取帧并调整其大小
    # 最大宽度为 400 像素
    frame = vs.read()
    frame = imutils.resize(frame, width=400)

    # 获取帧尺寸并将其转换为 blob
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
    0.007843, (300, 300), 127.5)

# 通过网络传递blob并获得检测
# 预测
net.setInput(blob)
detections = net.forward()

# 循环检测
for i in np.arange(0, detections.shape[2]):
    # 提取与相关联的置信度（即概率）用来预测
    confidence = detections[0, 0, i, 2]
    # 通过确保置信度大于最小置信度来过滤无效检测
if confidence &gt; args["confidence"]:
    # 从类标签 detections中提取索引，然后计算物体的边界框 (x, y) 坐标
    idx = int(detections[0, 0, i, 1])
    box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
    (startX, startY, endX, endY) = box.astype("int")

    # 在当前帧上绘制预测
    label = "{}: {:.2f}%".format(CLASSES[idx],
        confidence * 100)
    cv2.rectangle(frame, (startX, startY), (endX, endY),
        COLORS[idx], 2)
    y = startY - 15 if startY - 15 &gt; 15 else startY + 15
    cv2.putText(frame, label, (startX, y),
        cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
    # 显示输出帧
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF

# 如果按下 `q` 键，则跳出循环
if key == ord("q"):
    break

# 更新 FPS 计数器
fps.update()

    停止定时器并显示 FPS 信息

    fps.stop()
    print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
    print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

    做一些清理

    cv2.destroyAllWindows()
    vs.stop()

  • 调用方法:

    用法

    python real_time_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel

  • 测试效果：

3.配置文件

设置卷积层及其模型相关配置

  • MobileNetSSD_deploy.prototxt.txt

    name: "MobileNet-SSD"
    input: "data"
    input_shape {
    dim: 1
    dim: 3
    dim: 300
    dim: 300
    }
    layer {
    name: "conv0"
    type: "Convolution"
    bottom: "data"
    top: "conv0"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv0/relu"
    type: "ReLU"
    bottom: "conv0"
    top: "conv0"
    }
    layer {
    name: "conv1/dw"
    type: "Convolution"
    bottom: "conv0"
    top: "conv1/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    group: 32
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv1/dw/relu"
    type: "ReLU"
    bottom: "conv1/dw"
    top: "conv1/dw"
    }
    layer {
    name: "conv1"
    type: "Convolution"
    bottom: "conv1/dw"
    top: "conv1"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv1/relu"
    type: "ReLU"
    bottom: "conv1"
    top: "conv1"
    }
    layer {
    name: "conv2/dw"
    type: "Convolution"
    bottom: "conv1"
    top: "conv2/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 2
    group: 64
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv2/dw/relu"
    type: "ReLU"
    bottom: "conv2/dw"
    top: "conv2/dw"
    }
    layer {
    name: "conv2"
    type: "Convolution"
    bottom: "conv2/dw"
    top: "conv2"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv2/relu"
    type: "ReLU"
    bottom: "conv2"
    top: "conv2"
    }
    layer {
    name: "conv3/dw"
    type: "Convolution"
    bottom: "conv2"
    top: "conv3/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    group: 128
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv3/dw/relu"
    type: "ReLU"
    bottom: "conv3/dw"
    top: "conv3/dw"
    }
    layer {
    name: "conv3"
    type: "Convolution"
    bottom: "conv3/dw"
    top: "conv3"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv3/relu"
    type: "ReLU"
    bottom: "conv3"
    top: "conv3"
    }
    layer {
    name: "conv4/dw"
    type: "Convolution"
    bottom: "conv3"
    top: "conv4/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 2
    group: 128
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv4/dw/relu"
    type: "ReLU"
    bottom: "conv4/dw"
    top: "conv4/dw"
    }
    layer {
    name: "conv4"
    type: "Convolution"
    bottom: "conv4/dw"
    top: "conv4"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 256
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv4/relu"
    type: "ReLU"
    bottom: "conv4"
    top: "conv4"
    }
    layer {
    name: "conv5/dw"
    type: "Convolution"
    bottom: "conv4"
    top: "conv5/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 256
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv5/dw/relu"
    type: "ReLU"
    bottom: "conv5/dw"
    top: "conv5/dw"
    }
    layer {
    name: "conv5"
    type: "Convolution"
    bottom: "conv5/dw"
    top: "conv5"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 256
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv5/relu"
    type: "ReLU"
    bottom: "conv5"
    top: "conv5"
    }
    layer {
    name: "conv6/dw"
    type: "Convolution"
    bottom: "conv5"
    top: "conv6/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 2
    group: 256
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv6/dw/relu"
    type: "ReLU"
    bottom: "conv6/dw"
    top: "conv6/dw"
    }
    layer {
    name: "conv6"
    type: "Convolution"
    bottom: "conv6/dw"
    top: "conv6"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv6/relu"
    type: "ReLU"
    bottom: "conv6"
    top: "conv6"
    }
    layer {
    name: "conv7/dw"
    type: "Convolution"
    bottom: "conv6"
    top: "conv7/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv7/dw/relu"
    type: "ReLU"
    bottom: "conv7/dw"
    top: "conv7/dw"
    }
    layer {
    name: "conv7"
    type: "Convolution"
    bottom: "conv7/dw"
    top: "conv7"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv7/relu"
    type: "ReLU"
    bottom: "conv7"
    top: "conv7"
    }
    layer {
    name: "conv8/dw"
    type: "Convolution"
    bottom: "conv7"
    top: "conv8/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv8/dw/relu"
    type: "ReLU"
    bottom: "conv8/dw"
    top: "conv8/dw"
    }
    layer {
    name: "conv8"
    type: "Convolution"
    bottom: "conv8/dw"
    top: "conv8"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv8/relu"
    type: "ReLU"
    bottom: "conv8"
    top: "conv8"
    }
    layer {
    name: "conv9/dw"
    type: "Convolution"
    bottom: "conv8"
    top: "conv9/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv9/dw/relu"
    type: "ReLU"
    bottom: "conv9/dw"
    top: "conv9/dw"
    }
    layer {
    name: "conv9"
    type: "Convolution"
    bottom: "conv9/dw"
    top: "conv9"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv9/relu"
    type: "ReLU"
    bottom: "conv9"
    top: "conv9"
    }
    layer {
    name: "conv10/dw"
    type: "Convolution"
    bottom: "conv9"
    top: "conv10/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv10/dw/relu"
    type: "ReLU"
    bottom: "conv10/dw"
    top: "conv10/dw"
    }
    layer {
    name: "conv10"
    type: "Convolution"
    bottom: "conv10/dw"
    top: "conv10"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv10/relu"
    type: "ReLU"
    bottom: "conv10"
    top: "conv10"
    }
    layer {
    name: "conv11/dw"
    type: "Convolution"
    bottom: "conv10"
    top: "conv11/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv11/dw/relu"
    type: "ReLU"
    bottom: "conv11/dw"
    top: "conv11/dw"
    }
    layer {
    name: "conv11"
    type: "Convolution"
    bottom: "conv11/dw"
    top: "conv11"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv11/relu"
    type: "ReLU"
    bottom: "conv11"
    top: "conv11"
    }
    layer {
    name: "conv12/dw"
    type: "Convolution"
    bottom: "conv11"
    top: "conv12/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 2
    group: 512
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv12/dw/relu"
    type: "ReLU"
    bottom: "conv12/dw"
    top: "conv12/dw"
    }
    layer {
    name: "conv12"
    type: "Convolution"
    bottom: "conv12/dw"
    top: "conv12"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 1024
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv12/relu"
    type: "ReLU"
    bottom: "conv12"
    top: "conv12"
    }
    layer {
    name: "conv13/dw"
    type: "Convolution"
    bottom: "conv12"
    top: "conv13/dw"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 1024
    pad: 1
    kernel_size: 3
    group: 1024
    engine: CAFFE
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv13/dw/relu"
    type: "ReLU"
    bottom: "conv13/dw"
    top: "conv13/dw"
    }
    layer {
    name: "conv13"
    type: "Convolution"
    bottom: "conv13/dw"
    top: "conv13"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 1024
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv13/relu"
    type: "ReLU"
    bottom: "conv13"
    top: "conv13"
    }
    layer {
    name: "conv14_1"
    type: "Convolution"
    bottom: "conv13"
    top: "conv14_1"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 256
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv14_1/relu"
    type: "ReLU"
    bottom: "conv14_1"
    top: "conv14_1"
    }
    layer {
    name: "conv14_2"
    type: "Convolution"
    bottom: "conv14_1"
    top: "conv14_2"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv14_2/relu"
    type: "ReLU"
    bottom: "conv14_2"
    top: "conv14_2"
    }
    layer {
    name: "conv15_1"
    type: "Convolution"
    bottom: "conv14_2"
    top: "conv15_1"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv15_1/relu"
    type: "ReLU"
    bottom: "conv15_1"
    top: "conv15_1"
    }
    layer {
    name: "conv15_2"
    type: "Convolution"
    bottom: "conv15_1"
    top: "conv15_2"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv15_2/relu"
    type: "ReLU"
    bottom: "conv15_2"
    top: "conv15_2"
    }
    layer {
    name: "conv16_1"
    type: "Convolution"
    bottom: "conv15_2"
    top: "conv16_1"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv16_1/relu"
    type: "ReLU"
    bottom: "conv16_1"
    top: "conv16_1"
    }
    layer {
    name: "conv16_2"
    type: "Convolution"
    bottom: "conv16_1"
    top: "conv16_2"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv16_2/relu"
    type: "ReLU"
    bottom: "conv16_2"
    top: "conv16_2"
    }
    layer {
    name: "conv17_1"
    type: "Convolution"
    bottom: "conv16_2"
    top: "conv17_1"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv17_1/relu"
    type: "ReLU"
    bottom: "conv17_1"
    top: "conv17_1"
    }
    layer {
    name: "conv17_2"
    type: "Convolution"
    bottom: "conv17_1"
    top: "conv17_2"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv17_2/relu"
    type: "ReLU"
    bottom: "conv17_2"
    top: "conv17_2"
    }
    layer {
    name: "conv11_mbox_loc"
    type: "Convolution"
    bottom: "conv11"
    top: "conv11_mbox_loc"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 12
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv11_mbox_loc_perm"
    type: "Permute"
    bottom: "conv11_mbox_loc"
    top: "conv11_mbox_loc_perm"
    permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
    }
    }
    layer {
    name: "conv11_mbox_loc_flat"
    type: "Flatten"
    bottom: "conv11_mbox_loc_perm"
    top: "conv11_mbox_loc_flat"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "conv11_mbox_conf"
    type: "Convolution"
    bottom: "conv11"
    top: "conv11_mbox_conf"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 63
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv11_mbox_conf_perm"
    type: "Permute"
    bottom: "conv11_mbox_conf"
    top: "conv11_mbox_conf_perm"
    permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
    }
    }
    layer {
    name: "conv11_mbox_conf_flat"
    type: "Flatten"
    bottom: "conv11_mbox_conf_perm"
    top: "conv11_mbox_conf_flat"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "conv11_mbox_priorbox"
    type: "PriorBox"
    bottom: "conv11"
    bottom: "data"
    top: "conv11_mbox_priorbox"
    prior_box_param {
    min_size: 60.0
    aspect_ratio: 2.0
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    offset: 0.5
    }
    }
    layer {
    name: "conv13_mbox_loc"
    type: "Convolution"
    bottom: "conv13"
    top: "conv13_mbox_loc"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 24
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv13_mbox_loc_perm"
    type: "Permute"
    bottom: "conv13_mbox_loc"
    top: "conv13_mbox_loc_perm"
    permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
    }
    }
    layer {
    name: "conv13_mbox_loc_flat"
    type: "Flatten"
    bottom: "conv13_mbox_loc_perm"
    top: "conv13_mbox_loc_flat"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "conv13_mbox_conf"
    type: "Convolution"
    bottom: "conv13"
    top: "conv13_mbox_conf"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 126
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv13_mbox_conf_perm"
    type: "Permute"
    bottom: "conv13_mbox_conf"
    top: "conv13_mbox_conf_perm"
    permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
    }
    }
    layer {
    name: "conv13_mbox_conf_flat"
    type: "Flatten"
    bottom: "conv13_mbox_conf_perm"
    top: "conv13_mbox_conf_flat"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "conv13_mbox_priorbox"
    type: "PriorBox"
    bottom: "conv13"
    bottom: "data"
    top: "conv13_mbox_priorbox"
    prior_box_param {
    min_size: 105.0
    max_size: 150.0
    aspect_ratio: 2.0
    aspect_ratio: 3.0
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    offset: 0.5
    }
    }
    layer {
    name: "conv14_2_mbox_loc"
    type: "Convolution"
    bottom: "conv14_2"
    top: "conv14_2_mbox_loc"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 24
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv14_2_mbox_loc_perm"
    type: "Permute"
    bottom: "conv14_2_mbox_loc"
    top: "conv14_2_mbox_loc_perm"
    permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
    }
    }
    layer {
    name: "conv14_2_mbox_loc_flat"
    type: "Flatten"
    bottom: "conv14_2_mbox_loc_perm"
    top: "conv14_2_mbox_loc_flat"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "conv14_2_mbox_conf"
    type: "Convolution"
    bottom: "conv14_2"
    top: "conv14_2_mbox_conf"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 126
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv14_2_mbox_conf_perm"
    type: "Permute"
    bottom: "conv14_2_mbox_conf"
    top: "conv14_2_mbox_conf_perm"
    permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
    }
    }
    layer {
    name: "conv14_2_mbox_conf_flat"
    type: "Flatten"
    bottom: "conv14_2_mbox_conf_perm"
    top: "conv14_2_mbox_conf_flat"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "conv14_2_mbox_priorbox"
    type: "PriorBox"
    bottom: "conv14_2"
    bottom: "data"
    top: "conv14_2_mbox_priorbox"
    prior_box_param {
    min_size: 150.0
    max_size: 195.0
    aspect_ratio: 2.0
    aspect_ratio: 3.0
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    offset: 0.5
    }
    }
    layer {
    name: "conv15_2_mbox_loc"
    type: "Convolution"
    bottom: "conv15_2"
    top: "conv15_2_mbox_loc"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 24
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv15_2_mbox_loc_perm"
    type: "Permute"
    bottom: "conv15_2_mbox_loc"
    top: "conv15_2_mbox_loc_perm"
    permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
    }
    }
    layer {
    name: "conv15_2_mbox_loc_flat"
    type: "Flatten"
    bottom: "conv15_2_mbox_loc_perm"
    top: "conv15_2_mbox_loc_flat"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "conv15_2_mbox_conf"
    type: "Convolution"
    bottom: "conv15_2"
    top: "conv15_2_mbox_conf"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 126
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv15_2_mbox_conf_perm"
    type: "Permute"
    bottom: "conv15_2_mbox_conf"
    top: "conv15_2_mbox_conf_perm"
    permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
    }
    }
    layer {
    name: "conv15_2_mbox_conf_flat"
    type: "Flatten"
    bottom: "conv15_2_mbox_conf_perm"
    top: "conv15_2_mbox_conf_flat"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "conv15_2_mbox_priorbox"
    type: "PriorBox"
    bottom: "conv15_2"
    bottom: "data"
    top: "conv15_2_mbox_priorbox"
    prior_box_param {
    min_size: 195.0
    max_size: 240.0
    aspect_ratio: 2.0
    aspect_ratio: 3.0
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    offset: 0.5
    }
    }
    layer {
    name: "conv16_2_mbox_loc"
    type: "Convolution"
    bottom: "conv16_2"
    top: "conv16_2_mbox_loc"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 24
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv16_2_mbox_loc_perm"
    type: "Permute"
    bottom: "conv16_2_mbox_loc"
    top: "conv16_2_mbox_loc_perm"
    permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
    }
    }
    layer {
    name: "conv16_2_mbox_loc_flat"
    type: "Flatten"
    bottom: "conv16_2_mbox_loc_perm"
    top: "conv16_2_mbox_loc_flat"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "conv16_2_mbox_conf"
    type: "Convolution"
    bottom: "conv16_2"
    top: "conv16_2_mbox_conf"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 126
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv16_2_mbox_conf_perm"
    type: "Permute"
    bottom: "conv16_2_mbox_conf"
    top: "conv16_2_mbox_conf_perm"
    permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
    }
    }
    layer {
    name: "conv16_2_mbox_conf_flat"
    type: "Flatten"
    bottom: "conv16_2_mbox_conf_perm"
    top: "conv16_2_mbox_conf_flat"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "conv16_2_mbox_priorbox"
    type: "PriorBox"
    bottom: "conv16_2"
    bottom: "data"
    top: "conv16_2_mbox_priorbox"
    prior_box_param {
    min_size: 240.0
    max_size: 285.0
    aspect_ratio: 2.0
    aspect_ratio: 3.0
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    offset: 0.5
    }
    }
    layer {
    name: "conv17_2_mbox_loc"
    type: "Convolution"
    bottom: "conv17_2"
    top: "conv17_2_mbox_loc"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 24
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv17_2_mbox_loc_perm"
    type: "Permute"
    bottom: "conv17_2_mbox_loc"
    top: "conv17_2_mbox_loc_perm"
    permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
    }
    }
    layer {
    name: "conv17_2_mbox_loc_flat"
    type: "Flatten"
    bottom: "conv17_2_mbox_loc_perm"
    top: "conv17_2_mbox_loc_flat"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "conv17_2_mbox_conf"
    type: "Convolution"
    bottom: "conv17_2"
    top: "conv17_2_mbox_conf"
    param {
    lr_mult: 1.0
    decay_mult: 1.0
    }
    param {
    lr_mult: 2.0
    decay_mult: 0.0
    }
    convolution_param {
    num_output: 126
    kernel_size: 1
    weight_filler {
    type: "msra"
    }
    bias_filler {
    type: "constant"
    value: 0.0
    }
    }
    }
    layer {
    name: "conv17_2_mbox_conf_perm"
    type: "Permute"
    bottom: "conv17_2_mbox_conf"
    top: "conv17_2_mbox_conf_perm"
    permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
    }
    }
    layer {
    name: "conv17_2_mbox_conf_flat"
    type: "Flatten"
    bottom: "conv17_2_mbox_conf_perm"
    top: "conv17_2_mbox_conf_flat"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "conv17_2_mbox_priorbox"
    type: "PriorBox"
    bottom: "conv17_2"
    bottom: "data"
    top: "conv17_2_mbox_priorbox"
    prior_box_param {
    min_size: 285.0
    max_size: 300.0
    aspect_ratio: 2.0
    aspect_ratio: 3.0
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    offset: 0.5
    }
    }
    layer {
    name: "mbox_loc"
    type: "Concat"
    bottom: "conv11_mbox_loc_flat"
    bottom: "conv13_mbox_loc_flat"
    bottom: "conv14_2_mbox_loc_flat"
    bottom: "conv15_2_mbox_loc_flat"
    bottom: "conv16_2_mbox_loc_flat"
    bottom: "conv17_2_mbox_loc_flat"
    top: "mbox_loc"
    concat_param {
    axis: 1
    }
    }
    layer {
    name: "mbox_conf"
    type: "Concat"
    bottom: "conv11_mbox_conf_flat"
    bottom: "conv13_mbox_conf_flat"
    bottom: "conv14_2_mbox_conf_flat"
    bottom: "conv15_2_mbox_conf_flat"
    bottom: "conv16_2_mbox_conf_flat"
    bottom: "conv17_2_mbox_conf_flat"
    top: "mbox_conf"
    concat_param {
    axis: 1
    }
    }
    layer {
    name: "mbox_priorbox"
    type: "Concat"
    bottom: "conv11_mbox_priorbox"
    bottom: "conv13_mbox_priorbox"
    bottom: "conv14_2_mbox_priorbox"
    bottom: "conv15_2_mbox_priorbox"
    bottom: "conv16_2_mbox_priorbox"
    bottom: "conv17_2_mbox_priorbox"
    top: "mbox_priorbox"
    concat_param {
    axis: 2
    }
    }
    layer {
    name: "mbox_conf_reshape"
    type: "Reshape"
    bottom: "mbox_conf"
    top: "mbox_conf_reshape"
    reshape_param {
    shape {
    dim: 0
    dim: -1
    dim: 21
    }
    }
    }
    layer {
    name: "mbox_conf_softmax"
    type: "Softmax"
    bottom: "mbox_conf_reshape"
    top: "mbox_conf_softmax"
    softmax_param {
    axis: 2
    }
    }
    layer {
    name: "mbox_conf_flatten"
    type: "Flatten"
    bottom: "mbox_conf_softmax"
    top: "mbox_conf_flatten"
    flatten_param {
    axis: 1
    }
    }
    layer {
    name: "detection_out"
    type: "DetectionOutput"
    bottom: "mbox_loc"
    bottom: "mbox_conf_flatten"
    bottom: "mbox_priorbox"
    top: "detection_out"
    include {
    phase: TEST
    }
    detection_output_param {
    num_classes: 21
    share_location: true
    background_label_id: 0
    nms_param {
    nms_threshold: 0.45
    top_k: 100
    }
    code_type: CENTER_SIZE
    keep_top_k: 100
    confidence_threshold: 0.25
    }
    }

4.模型下载

MobileNetSSD_deploy.caffemodel: https://share.weiyun.com/Wi04sqO7

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