经典卷积模型回顾33—利用YOLOv3实现垃圾检测(Tensorflow2.0)

YOLOv3(You Only Look Once version 3,全称:“你只看一次”第3版)是一种物体检测算法,它是YOLO系列算法的第三个版本。YOLOv3是由Joseph Redmon和Ali Farhadi于2018年推出的。

相比于前两个版本,YOLOv3具有更高的检测精度和更好的性能。它使用了一些新的技术,包括残差块和跨层连接,以提高特征提取的效果,从而提高检测精度。

YOLOv3模型分为两个部分:特征提取网络和检测网络。特征提取网络使用Darknet-53架构,可以快速、准确地提取输入图像的特征。检测网络是基于特征提取网络的输出,通过多个检测层来预测不同尺度下的目标框、置信度和类别,进而完成物体检测的任务。

YOLOv3的优点在于速度快、可用于实时检测,并且可以同时检测多个目标,不需要对输入图像进行预处理。与传统的基于区域的物体检测方法相比,YOLOv3还具有更好的鲁棒性和更高的检测精度,可以适用于更多的应用场景。

下面是使用YOLOv3进行垃圾检测的步骤:


1. 准备垃圾图像数据集,并将其分为训练集、验证集和测试集。
2. 训练YOLOv3模型。您可以使用已经实现的YOLOv3代码库并根据您的数据集进行调整。在训练期间,您可以通过查看训练损失和预测结果来监控模型的性能。
3. 进行模型优化和测试。使用验证集来优化模型并通过测试集来测试模型的性能。
4. 使用模型进行垃圾检测。使用模型来预测新图像中的垃圾物体,并将其与垃圾图像数据集中的标签进行比较。
 

以下是基于TensorFlow 2.0的YOLOv3垃圾检测例

1. 安装TensorFlow 2.0和相关依赖库:


pip install tensorflow==2.0.0
pip install opencv-python
pip install matplotlib
 

2. 下载YOLOv3的权重文件yolov3.weights和相关配置文件:


wget https://pjreddie.com/media/files/yolov3.weights
wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg
wget https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names
 

3. 使用OpenCV加载图片,并对其进行预处理:

import cv2
import numpy as np

def load_image(img_path):
    img = cv2.imread(img_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, (416, 416))
    img = img.astype(np.float32)
    img = img / 255.0
    img = np.expand_dims(img, axis=0)
    return img

4. 创建YOLOv3模型:

import tensorflow as tf

def create_yolo_model():
    input_layer = tf.keras.layers.Input([416, 416, 3])
    conv_layer_1 = tf.keras.layers.Conv2D(32, (3, 3), strides=(1, 1), padding='same', activation='relu')(input_layer)
    pool_layer_1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(conv_layer_1)
    conv_layer_2 = tf.keras.layers.Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu')(pool_layer_1)
    pool_layer_2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(conv_layer_2)
    conv_layer_3 = tf.keras.layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu')(pool_layer_2)
    conv_layer_4 = tf.keras.layers.Conv2D(64, (1, 1), strides=(1, 1), padding='same', activation='relu')(conv_layer_3)
    conv_layer_5 = tf.keras.layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu')(conv_layer_4)
    pool_layer_3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(conv_layer_5)
    conv_layer_6 = tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')(pool_layer_3)
    conv_layer_7 = tf.keras.layers.Conv2D(128, (1, 1), strides=(1, 1), padding='same', activation='relu')(conv_layer_6)
    conv_layer_8 = tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')(conv_layer_7)
    pool_layer_4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(conv_layer_8)
    conv_layer_9 = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')(pool_layer_4)
    conv_layer_10 = tf.keras.layers.Conv2D(256, (1, 1), strides=(1, 1), padding='same', activation='relu')(conv_layer_9)
    conv_layer_11 = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')(conv_layer_10)
    conv_layer_12 = tf.keras.layers.Conv2D(256, (1, 1), strides=(1, 1), padding='same', activation='relu')(conv_layer_11)
    conv_layer_13 = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')(conv_layer_12)
    skip_connection = conv_layer_13
    pool_layer_5 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(conv_layer_13)
    conv_layer_14 = tf.keras.layers.Conv2D(1024, (3, 3), strides=(1, 1), padding='same', activation='relu')(pool_layer_5)
    conv_layer_15 = tf.keras.layers.Conv2D(512, (1, 1), strides=(1, 1), padding='same', activation='relu')(conv_layer_14)
    conv_layer_16 = tf.keras.layers.Conv2D(1024, (3, 3), strides=(1, 1), padding='same', activation='relu')(conv_layer_15)
    conv_layer_17 = tf.keras.layers.Conv2D(512, (1, 1), strides=(1, 1), padding='same', activation='relu')(conv_layer_16)
    conv_layer_18 = tf.keras.layers.Conv2D(1024, (3, 3), strides=(1, 1), padding='same', activation='relu')(conv_layer_17)
    conv_layer_19 = tf.keras.layers.Conv2D(1024, (3, 3), strides=(1, 1), padding='same', activation='relu')(conv_layer_18)
    conv_layer_20 = tf.keras.layers.Conv2D(1024, (3, 3), strides=(1, 1), padding='same', activation='relu')(skip_connection)
    concatenate_layer = tf.keras.layers.concatenate([conv_layer_20, conv_layer_19], axis=-1)
    conv_layer_21 = tf.keras.layers.Conv2D(1024, (3, 3), strides=(1, 1), padding='same', activation='relu')(concatenate_layer)
    flatten_layer = tf.keras.layers.Flatten()(conv_layer_21)
    dense_layer_1 = tf.keras.layers.Dense(4096, activation='relu')(flatten_layer)
    dropout_layer_1 = tf.keras.layers.Dropout(0.5)(dense_layer_1)
    output_layer = tf.keras.layers.Dense(2535, activation='softmax')(dropout_layer_1)
    return tf.keras.Model(inputs=input_layer, outputs=output_layer)

5. 加载YOLOv3权重文件:

import struct

def load_weights(model, weights_file):
    with open(weights_file, "rb") as f:
        # Skip header
        np.fromfile(f, dtype=np.int32, count=5)

        for layer in model.layers:
            if not layer.name.startswith('conv2d'):
                continue

            filters = layer.filters
            k_size = layer.kernel_size[0]
            in_dim = layer.input_shape[-1]

            if layer.activation == tf.keras.activations.linear:
                # Darknet serializes convolutional weights as:
                # [bias/beta, [gamma, mean, variance], conv_weights]
                # We need to split them up and flatten the conv_weights
                conv_bias = np.fromfile(f, dtype=np.float32, count=filters)
                bn_weights = np.fromfile(f, dtype=np.float32, count=4 * filters)
                bn_weights = bn_weights.reshape((4, filters))[[1, 0, 2, 3]]

                kernel_shape = (k_size, k_size, in_dim, filters)
                conv_weights = np.fromfile(f, dtype=np.float32, count=np.product(kernel_shape))
                conv_weights = conv_weights.reshape(kernel_shape).transpose([2, 3, 1, 0])

                layer.set_weights([conv_weights, conv_bias, bn_weights])
            else:
                # Load conv. bias
                conv_bias = np.fromfile(f, dtype=np.float32, count=filters)

                # Load conv. weights
                kernel_shape = (k_size, k_size, in_dim, filters)
                conv_weights = np.fromfile(f, dtype=np.float32, count=np.product(kernel_shape))
                conv_weights = conv_weights.reshape(kernel_shape).transpose([2, 3, 1, 0])

                layer.set_weights([conv_weights, conv_bias])

6. 加载类别名称:

def load_class_names(names_file):
    with open(names_file, "r") as f:
        class_names = f.read().splitlines()
    return class_names

7. 进行预测:

def predict(image_file, model, class_names):
    # Load image
    image_data = load_image(image_file)

    # Predict
    pred = model.predict(image_data)

    # Decode prediction
    boxes, objectness, classes = tf.split(pred, (4, 1, -1), axis=-1)
    boxes = decode_boxes(boxes)
    scores = objectness * classes
    scores = tf.reshape(scores, shape=(-1,))
    boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(
        boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),
        scores=tf.reshape(scores, (tf.shape(scores)[0], -1, tf.shape(scores)[-1])),
        max_output_size_per_class=50,
        max_total_size=50,
        iou_threshold=0.5,
        score_threshold=0.5
    )
    pred_bbox = [boxes.numpy(), scores.numpy(), classes.numpy(), valid_detections.numpy()]

    # Visualize prediction
    visualize_prediction(image_file, pred_bbox, class_names)

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