乘员分类与年龄性别检测：CNN深度学习方法

研究背景

Euro NCAP 2026要求：

• OMS必须识别乘员类型（成人/儿童/婴儿）
• 用于安全带提醒、气囊适配
• 儿童座椅检测（后向/前向）

应用场景：

应用	分类需求	精度要求
安全带提醒	成人/儿童区分	>95%
气囊适配	儿童→禁用前排气囊	>99%
CPD儿童检测	儿童/婴儿检测	>90%
个性化服务	年龄/性别	>85%

技术方案

1. 数据集

公开数据集：

数据集	样本数	年龄范围	特点
MORPH-II	55,000	16-77	年龄标注详细
FG-NET	1,002	0-69	包含儿童
IMDB-WIKI	500,000+	0-100	大规模
SVIRO	车内场景	多年龄	车内专用

2. 网络架构

多任务CNN架构：

"""
乘员分类CNN模型
多任务学习：年龄估计 + 性别分类 + 儿童/成人区分
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Dict

class OccupantClassifier(nn.Module):
    """
    乘员分类器
    
    输出：
    1. 年龄估计（回归）
    2. 性别分类（二分类）
    3. 年龄组分类（多分类：儿童/青少年/成人/老年）
    4. 儿童座椅检测（二分类）
    """
    
    def __init__(self, 
                 backbone: str = 'resnet50',
                 pretrained: bool = True):
        """
        Args:
            backbone: 骨干网络
            pretrained: 是否使用预训练权重
        """
        super().__init__()
        
        # 骨干网络
        if backbone == 'resnet50':
            from torchvision.models import resnet50, ResNet50_Weights
            self.backbone = resnet50(
                weights=ResNet50_Weights.DEFAULT if pretrained else None
            )
            feature_dim = 2048
            
        elif backbone == 'efficientnet_b0':
            from torchvision.models import efficientnet_b0, EfficientNet_B0_Weights
            self.backbone = efficientnet_b0(
                weights=EfficientNet_B0_Weights.DEFAULT if pretrained else None
            )
            feature_dim = 1280
            
        # 移除原始分类头
        self.backbone.fc = nn.Identity()
        
        # 共享特征层
        self.shared_fc = nn.Sequential(
            nn.Linear(feature_dim, 512),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(512, 256),
            nn.ReLU(),
        )
        
        # 年龄回归头
        self.age_head = nn.Sequential(
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, 1),
            nn.ReLU()  # 年龄非负
        )
        
        # 性别分类头
        self.gender_head = nn.Sequential(
            nn.Linear(256, 64),
            nn.ReLU(),
            nn.Linear(64, 2)  # male/female
        )
        
        # 年龄组分类头
        self.age_group_head = nn.Sequential(
            nn.Linear(256, 64),
            nn.ReLU(),
            nn.Linear(64, 5)  # 0-3, 4-12, 13-17, 18-60, 60+
        )
        
        # 儿童座椅检测头
        self.child_seat_head = nn.Sequential(
            nn.Linear(256, 64),
            nn.ReLU(),
            nn.Linear(64, 3)  # none/rear-facing/forward-facing
        )
        
    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        """
        前向传播
        
        Args:
            x: 输入图像 (B, 3, H, W)
            
        Returns:
            outputs: 各任务输出
        """
        # 提取特征
        features = self.backbone(x)
        
        # 共享层
        shared = self.shared_fc(features)
        
        # 各任务输出
        age = self.age_head(shared).squeeze(-1)
        gender = self.gender_head(shared)
        age_group = self.age_group_head(shared)
        child_seat = self.child_seat_head(shared)
        
        return {
            'age': age,
            'gender': gender,
            'age_group': age_group,
            'child_seat': child_seat
        }
    
    def predict(self, x: torch.Tensor) -> Dict:
        """
        推理预测
        
        Returns:
            predictions: 预测结果字典
        """
        self.eval()
        with torch.no_grad():
            outputs = self.forward(x)
            
            # 年龄
            age = outputs['age'].cpu().numpy()
            
            # 性别
            gender_prob = F.softmax(outputs['gender'], dim=1)
            gender = torch.argmax(gender_prob, dim=1).cpu().numpy()
            gender_conf = torch.max(gender_prob, dim=1)[0].cpu().numpy()
            
            # 年龄组
            age_group_names = ['infant', 'child', 'teenager', 'adult', 'senior']
            age_group_prob = F.softmax(outputs['age_group'], dim=1)
            age_group_idx = torch.argmax(age_group_prob, dim=1).cpu().numpy()
            age_group = [age_group_names[i] for i in age_group_idx]
            
            # 儿童座椅
            seat_names = ['none', 'rear-facing', 'forward-facing']
            seat_prob = F.softmax(outputs['child_seat'], dim=1)
            seat_idx = torch.argmax(seat_prob, dim=1).cpu().numpy()
            child_seat = [seat_names[i] for i in seat_idx]
            
        return {
            'age': age.tolist(),
            'gender': ['male' if g == 0 else 'female' for g in gender],
            'gender_confidence': gender_conf.tolist(),
            'age_group': age_group,
            'child_seat': child_seat
        }


class MultiTaskLoss(nn.Module):
    """多任务损失函数"""
    
    def __init__(self,
                 age_weight: float = 1.0,
                 gender_weight: float = 1.0,
                 age_group_weight: float = 1.0,
                 child_seat_weight: float = 1.0):
        super().__init__()
        
        self.age_weight = age_weight
        self.gender_weight = gender_weight
        self.age_group_weight = age_group_weight
        self.child_seat_weight = child_seat_weight
        
    def forward(self, 
                outputs: Dict[str, torch.Tensor],
                targets: Dict[str, torch.Tensor]) -> Tuple[torch.Tensor, Dict]:
        """
        计算总损失
        
        Args:
            outputs: 模型输出
            targets: 标签 {'age':, 'gender':, 'age_group':, 'child_seat':}
            
        Returns:
            total_loss: 总损失
            loss_dict: 各任务损失
        """
        # 年龄损失（L1）
        age_loss = F.l1_loss(outputs['age'], targets['age'])
        
        # 性别损失（交叉熵）
        gender_loss = F.cross_entropy(outputs['gender'], targets['gender'])
        
        # 年龄组损失
        age_group_loss = F.cross_entropy(outputs['age_group'], targets['age_group'])
        
        # 儿童座椅损失
        child_seat_loss = F.cross_entropy(outputs['child_seat'], targets['child_seat'])
        
        # 总损失
        total_loss = (
            self.age_weight * age_loss +
            self.gender_weight * gender_loss +
            self.age_group_weight * age_group_loss +
            self.child_seat_weight * child_seat_loss
        )
        
        loss_dict = {
            'age_loss': age_loss.item(),
            'gender_loss': gender_loss.item(),
            'age_group_loss': age_group_loss.item(),
            'child_seat_loss': child_seat_loss.item(),
            'total_loss': total_loss.item()
        }
        
        return total_loss, loss_dict


# 训练代码
def train_epoch(model, dataloader, optimizer, criterion, device):
    """训练一个epoch"""
    model.train()
    total_losses = {'age_loss': 0, 'gender_loss': 0, 'age_group_loss': 0, 
                    'child_seat_loss': 0, 'total_loss': 0}
    
    for batch in dataloader:
        images = batch['image'].to(device)
        targets = {
            'age': batch['age'].to(device),
            'gender': batch['gender'].to(device),
            'age_group': batch['age_group'].to(device),
            'child_seat': batch['child_seat'].to(device)
        }
        
        # 前向
        outputs = model(images)
        
        # 损失
        loss, loss_dict = criterion(outputs, targets)
        
        # 反向
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        # 累积损失
        for k, v in loss_dict.items():
            total_losses[k] += v
            
    # 平均
    n = len(dataloader)
    for k in total_losses:
        total_losses[k] /= n
        
    return total_losses


# 测试代码
if __name__ == "__main__":
    # 创建模型
    model = OccupantClassifier(backbone='resnet50', pretrained=True)
    
    # 模拟输入
    batch_size = 4
    x = torch.randn(batch_size, 3, 224, 224)
    
    # 推理
    predictions = model.predict(x)
    
    print("预测结果:")
    for i in range(batch_size):
        print(f"  样本{i}: 年龄={predictions['age'][i]:.1f}, "
              f"性别={predictions['gender'][i]}, "
              f"年龄组={predictions['age_group'][i]}, "
              f"儿童座椅={predictions['child_seat'][i]}")
    
    # 统计参数
    n_params = sum(p.numel() for p in model.parameters())
    print(f"\n模型参数量: {n_params/1e6:.2f}M")

3. 年龄组定义

Euro NCAP推荐分类：

年龄组	年龄范围	特征	安全配置
Infant	0-3岁	婴儿座椅	后排后向座椅
Child	4-12岁	儿童座椅	后排前向座椅
Teenager	13-17岁	可用成人安全带	正常配置
Adult	18-60岁	标准乘员	正常配置
Senior	60+岁	可能需要特殊配置	考虑气囊力度

4. 性能指标

基准结果：

方法	年龄MAE	性别准确率	儿童检测
ResNet50	5.77年	95%	92%
EfficientNet-B0	6.2年	93%	90%
MobileNetV3	7.1年	91%	88%

Euro NCAP测试场景

儿童座椅检测

场景编号	场景描述	检测要求
CS-01	后向婴儿座椅	≥95%
CS-02	前向儿童座椅	≥95%
CS-03	增高垫	≥90%
CS-04	无座椅儿童	≥85%

乘员分类测试

# Euro NCAP乘员分类测试
OCCUPANT_TEST_CASES = [
    {'age': 1, 'expected_group': 'infant', 'expected_seat': 'rear-facing'},
    {'age': 5, 'expected_group': 'child', 'expected_seat': 'forward-facing'},
    {'age': 15, 'expected_group': 'teenager', 'expected_seat': 'none'},
    {'age': 35, 'expected_group': 'adult', 'expected_seat': 'none'},
    {'age': 70, 'expected_group': 'senior', 'expected_seat': 'none'},
]

def test_occupant_classifier(model, test_cases):
    """测试乘员分类器"""
    results = []
    
    for case in test_cases:
        # 模拟图像
        # 实际需要采集真实图像
        image = generate_synthetic_face(case['age'])
        
        prediction = model.predict(image)
        
        correct_group = prediction['age_group'] == case['expected_group']
        correct_seat = prediction['child_seat'] == case['expected_seat']
        
        results.append({
            'age': case['age'],
            'expected_group': case['expected_group'],
            'predicted_group': prediction['age_group'],
            'group_correct': correct_group,
            'seat_correct': correct_seat
        })
        
    return results

IMS开发启示

1. 模型部署优化

量化与加速：

优化方法	精度损失	速度提升	内存减少
FP16量化	<1%	1.5-2x	50%
INT8量化	1-2%	2-4x	75%
剪枝50%	<2%	1.3x	50%
知识蒸馏	<1%	-	70%

2. 部署代码

import onnxruntime as ort

class ONNXOccupantClassifier:
    """ONNX部署版本"""
    
    def __init__(self, onnx_path: str):
        # 创建推理会话
        self.session = ort.InferenceSession(onnx_path)
        
        # 获取输入输出信息
        self.input_name = self.session.get_inputs()[0].name
        self.output_names = [o.name for o in self.session.get_outputs()]
        
    def predict(self, image: np.ndarray) -> Dict:
        """
        推理
        
        Args:
            image: 预处理后的图像 (1, 3, 224, 224)
            
        Returns:
            predictions: 预测结果
        """
        # ONNX推理
        outputs = self.session.run(self.output_names, 
                                   {self.input_name: image})
        
        # 解析输出
        age = outputs[0][0]
        gender_prob = outputs[1][0]
        age_group_prob = outputs[2][0]
        child_seat_prob = outputs[3][0]
        
        return {
            'age': float(age),
            'gender': 'female' if np.argmax(gender_prob) else 'male',
            'age_group': ['infant', 'child', 'teenager', 'adult', 'senior'][
                np.argmax(age_group_prob)
            ],
            'child_seat': ['none', 'rear-facing', 'forward-facing'][
                np.argmax(child_seat_prob)
            ]
        }

3. 实时性能

平台	FP32延迟	INT8延迟	功耗
QCS8255 (CPU)	45ms	20ms	0.5W
QCS8255 (DSP)	25ms	12ms	0.3W
QCS8295 (NPU)	8ms	5ms	0.4W
TI TDA4 (C7x)	30ms	15ms	0.35W

参考文献

1. Zhang, K., et al. “Age and Gender Prediction using Deep CNNs and Transfer Learning.” arXiv, 2021.
2. Da Cruz, L., et al. “SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset.” WACV 2020.
3. Euro NCAP. “Child Presence Detection Protocol v1.0.” 2025.

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