用U-Net实现自动驾驶车道线检测的代码

用U-Net实现自动驾驶车道线检测的代码通常是Python结合深度学习库TensorFlow或PyTorch编写。由于这是一个相对复杂的过程，涉及到深度学习模型构建、数据预处理、训练和评估等步骤，这里只能提供一个简化的框架示例。假设已经安装了相关的库：

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate

# U-Net结构
def unet(input_size=(None, None, 3)):
    inputs = Input(input_size)
    
    # 编码部分
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    # 中间层
    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool2)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    # 解码部分
    up4 = concatenate([UpSampling2D(size=(2, 2))(conv3), conv2], axis=3)
    conv4 = Conv2D(128, (3, 3), activation='relu', padding='same')(up4)
    conv4 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv4)

    up5 = concatenate([UpSampling2D(size=(2, 2))(conv4), conv1], axis=3)
    conv5 = Conv2D(64, (3, 3), activation='relu', padding='same')(up5)
    conv5 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv5)

    # 输出层
    outputs = Conv2D(1, (1, 1), activation='sigmoid')(conv5)  # Sigmoid用于二分类，输出每个像素的概率

    return Model(inputs=inputs, outputs=outputs)

# 初始化并编译模型
model = unet()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# 调整模型尺寸以适应数据大小
model.fit(train_data, train_labels, epochs=20, validation_data=(val_data, val_labels))

这个例子中，`train_data`和`train_labels`是训练数据集，包含原始图像和对应的车道线掩码；`val_data`和`val_labels`是验证数据集。注意实际项目中需要对数据进行预处理，例如归一化、裁剪和增强，以及可能的迁移学习调整。