无人机多模态目标识别代码示例

无人机多模态目标识别通常涉及结合视觉、雷达、红外等多种传感器的数据，以便更准确地识别人类或物体。以下是一个简化的Python代码示例，使用TensorFlow库来处理一个多模态深度学习模型（这里假设我们已经有了图像和雷达数据预处理函数`preprocess_image()`和`preprocess_radar()`）：

import tensorflow as tf
from tensorflow.keras import layers

class MultiModalModel(tf.keras.Model):
    def __init__(self):
        super(MultiModalModel, self).__init__()
        self.image_encoder = ResNet50(weights='imagenet', include_top=False)
        self.radar_encoder = RadarEncoder()  # 自定义雷达特征提取网络
        self.concat_layer = layers.Concatenate()
        self.shared_layers = [
            layers.Dense(512, activation='relu'),
            layers.Dropout(0.5),
            layers.Dense(256, activation='relu')
        ]
        self.classifier = layers.Dense(num_classes, activation='softmax')

    def call(self, inputs):
        image_input = preprocess_image(inputs['image'])
        radar_input = preprocess_radar(inputs['radar'])

        img_features = self.image_encoder(image_input)
        radar_features = self.radar_encoder(radar_input)

        combined = self.concat_layer([img_features, radar_features])
        for layer in self.shared_layers:
            combined = layer(combined)

        return self.classifier(combined)

# 使用示例
model = MultiModalModel()
inputs = {'image': images, 'radar': radar_data}  # 假设images和radar_data是张量
outputs = model(inputs)
loss, accuracy = model.compile_loss_and_metrics(outputs, labels)  # 假设labels是实际标签

在这个例子中，首先分别从图像和雷达数据中提取特征，然后通过concatenation层将两者结合，接着经过共享卷积神经网络层，最后由分类器预测类别。