驾驶员疲劳与分心机器学习检测改进：MobileNet CNN实时方案

论文信息

• 标题：Improving automatic detection of driver fatigue and distraction using machine learning
• 来源：arXiv:2401.10213
• 年份：2024年1月
• 关键方法：MobileNet CNN + 多任务学习

---

核心创新

本研究改进疲劳和分心检测：

1. MobileNet轻量化架构
2. 实时检测（>30fps）
3. 多任务联合训练
4. 隐私保护设计

---

方法详解

1. 网络架构

# MobileNet疲劳分心检测网络
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.mobilenetv3 import mobilenet_v3_small

class MobileNetFatigueDistraction(nn.Module):
    """
    MobileNet疲劳分心检测网络
    
    多任务输出：疲劳状态 + 分心类型
    """
    
    def __init__(self, num_fatigue_classes: int = 3, 
                 num_distraction_classes: int = 6):
        """
        Args:
            num_fatigue_classes: 疲劳等级（alert/drowsy/fatigued）
            num_distraction_classes: 分心类型（正常/手机/饮食/交谈/调节/其他）
        """
        super().__init__()
        
        # Backbone: MobileNetV3-Small
        mobilenet = mobilenet_v3_small(pretrained=True)
        self.features = mobilenet.features
        
        # 疲劳检测头
        self.fatigue_head = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(576, 128),
            nn.ReLU(inplace=True),
            nn.Dropout(0.2),
            nn.Linear(128, num_fatigue_classes)
        )
        
        # 分心检测头
        self.distraction_head = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(576, 128),
            nn.ReLU(inplace=True),
            nn.Dropout(0.2),
            nn.Linear(128, num_distraction_classes)
        )
        
        # 共享特征层（提升泛化）
        self.shared_fc = nn.Linear(576, 256)
    
    def forward(self, x: torch.Tensor) -> dict:
        """
        前向传播
        
        Args:
            x: 输入图像 (B, 3, 224, 224)
        
        Returns:
            output: {
                'fatigue_logits': (B, 3),
                'distraction_logits': (B, 6),
                'shared_features': (B, 256)
            }
        """
        # 特征提取
        features = self.features(x)
        
        # 共享特征
        shared = F.adaptive_avg_pool2d(features, 1).flatten(1)
        shared_features = F.relu(self.shared_fc(shared))
        
        # 多任务输出
        fatigue_logits = self.fatigue_head(features)
        distraction_logits = self.distraction_head(features)
        
        return {
            'fatigue_logits': fatigue_logits,
            'distraction_logits': distraction_logits,
            'shared_features': shared_features
        }


class MultiTaskLoss(nn.Module):
    """
    多任务损失函数
    
    疲劳 + 分心联合优化
    """
    
    def __init__(self, fatigue_weight: float = 1.0, 
                 distraction_weight: float = 1.0):
        super().__init__()
        
        self.fatigue_weight = fatigue_weight
        self.distraction_weight = distraction_weight
        
        self.ce_loss = nn.CrossEntropyLoss()
    
    def forward(self, predictions: dict, targets: dict) -> torch.Tensor:
        """
        计算损失
        
        Args:
            predictions: 模型输出
            targets: {
                'fatigue': (B,),
                'distraction': (B,)
            }
        """
        fatigue_loss = self.ce_loss(
            predictions['fatigue_logits'],
            targets['fatigue']
        )
        
        distraction_loss = self.ce_loss(
            predictions['distraction_logits'],
            targets['distraction']
        )
        
        total_loss = (self.fatigue_weight * fatigue_loss + 
                      self.distraction_weight * distraction_loss)
        
        return total_loss


# PERCLOS计算模块
class PERCLOSCalculator(nn.Module):
    """
    PERCLOS计算模块
    
    基于眼睑开度序列
    """
    
    def __init__(self, window_sec: int = 60, fps: int = 30):
        super().__init__()
        
        self.window_frames = window_sec * fps
        self.threshold = 0.2  # 闭眼阈值
    
    def forward(self, eye_openness: torch.Tensor) -> torch.Tensor:
        """
        计算PERCLOS
        
        Args:
            eye_openness: 眼睑开度序列 (B, T), 范围[0, 1]
        
        Returns:
            perclos: PERCLOS值 (B,)
        """
        B, T = eye_openness.shape
        
        # 滑动窗口
        perclos_values = []
        
        for i in range(T - self.window_frames):
            window = eye_openness[:, i:i+self.window_frames]
            
            # 闭眼比例
            closed = (window < self.threshold).float()
            perclos = closed.mean(dim=1)
            perclos_values.append(perclos)
        
        if len(perclos_values) > 0:
            return torch.stack(perclos_values, dim=1).mean(dim=1)
        else:
            return torch.zeros(B, device=eye_openness.device)


# 实时检测器
class RealTimeFatigueDetector:
    """
    实时疲劳分心检测器
    
    优化推理速度
    """
    
    def __init__(self, model_path: str, device: str = 'cpu'):
        self.model = MobileNetFatigueDistraction()
        self.model.load_state_dict(torch.load(model_path, map_location=device))
        self.model.eval()
        self.device = device
        
        self.model = self.model.to(device)
        
        # 预处理
        self.transform = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                std=[0.229, 0.224, 0.225])
        ])
    
    def detect(self, frame: np.ndarray) -> dict:
        """
        单帧检测
        
        Args:
            frame: 输入图像 (H, W, 3)
        
        Returns:
            result: {
                'fatigue_level': str,
                'distraction_type': str,
                'latency_ms': float
            }
        """
        import time
        
        start_time = time.time()
        
        # 预处理
        frame_pil = Image.fromarray(frame)
        input_tensor = self.transform(frame_pil).unsqueeze(0).to(self.device)
        
        # 推理
        with torch.no_grad():
            output = self.model(input_tensor)
        
        # 后处理
        fatigue_idx = output['fatigue_logits'].argmax(dim=1).item()
        distraction_idx = output['distraction_logits'].argmax(dim=1).item()
        
        fatigue_levels = ['alert', 'drowsy', 'fatigued']
        distraction_types = ['normal', 'phone', 'eating', 'talking', 'adjusting', 'other']
        
        latency_ms = (time.time() - start_time) * 1000
        
        return {
            'fatigue_level': fatigue_levels[fatigue_idx],
            'distraction_type': distraction_types[distraction_idx],
            'fatigue_confidence': F.softmax(output['fatigue_logits'], dim=1)[0, fatigue_idx].item(),
            'distraction_confidence': F.softmax(output['distraction_logits'], dim=1)[0, distraction_idx].item(),
            'latency_ms': latency_ms
        }


# 测试代码
if __name__ == "__main__":
    from torchvision import transforms
    from PIL import Image
    
    # 创建模型
    model = MobileNetFatigueDistraction()
    
    # 统计参数
    total_params = sum(p.numel() for p in model.parameters())
    print(f"模型参数: {total_params:,}")
    print(f"模型大小: {total_params * 4 / 1024 / 1024:.2f} MB")
    
    # 模拟输入
    x = torch.randn(1, 3, 224, 224)
    
    # 前向传播
    output = model(x)
    
    print(f"\n疲劳检测输出: {output['fatigue_logits'].shape}")
    print(f"分心检测输出: {output['distraction_logits'].shape}")
    print(f"共享特征: {output['shared_features'].shape}")

---

性能指标

论文实验结果

指标	数值	说明
疲劳检测准确率	94.8%	3分类
分心检测准确率	91.5%	6分类
推理速度	45fps	CPU
模型大小	2.5MB	MobileNetV3-Small
功耗	<1W	移动端

与其他方法对比

方法	准确率	速度	模型大小
ResNet-50	96.2%	15fps	98MB
MobileNetV3-Small	94.8%	45fps	2.5MB
EfficientNet-B0	95.5%	25fps	5.3MB

---

Euro NCAP疲劳分心检测对齐

场景映射

# Euro NCAP疲劳分心检测场景
FATIGUE_DISTRACTION_SCENARIOS = {
    "fatigue_detection": {
        "classes": ["alert", "drowsy", "fatigued"],
        "threshold": {
            "perclos": "> 30%",
            "duration": "> 60s"
        },
        "response_time": "< 3s",
        "scoring": 5分
    },
    "distraction_detection": {
        "classes": ["normal", "phone", "eating", "talking", "adjusting"],
        "threshold": {
            "duration": "> 3s"
        },
        "response_time": "< 2s",
        "scoring": 6分
    }
}

---

IMS开发启示

1. 模型选择策略

车型定位	推荐模型	准确率	推理速度
入门级	MobileNetV3-Small	94.8%	45fps
中端	EfficientNet-B0	95.5%	25fps
高端	ResNet-50	96.2%	15fps

2. 多任务训练策略

Multi_Task_Training_Config:
  loss_weights:
    fatigue: 1.0
    distraction: 1.0
  
  data_balance:
    fatigue_samples: "30% alert / 40% drowsy / 30% fatigued"
    distraction_samples: "40% normal / 60% distracted"
  
  augmentation:
    - "RandomBrightness (±30%)"
    - "RandomContrast (±20%)"
    - "MotionBlur (p=0.3)"
    - "Occlusion (p=0.2)"

3. 部署优化

# 量化部署
def quantize_for_deployment(model: nn.Module):
    """
    量化模型以减少部署成本
    
    Returns:
        quantized_model: INT8量化模型
    """
    model.eval()
    
    # 动态量化
    quantized_model = torch.quantization.quantize_dynamic(
        model,
        {nn.Linear},
        dtype=torch.qint8
    )
    
    return quantized_model


# 量化后性能提升
QUANTIZATION_BENEFITS = {
    "model_size": "2.5MB → 0.7MB",
    "inference_speed": "45fps → 60fps",
    "accuracy_loss": "< 1%"
}

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参考资源

• 论文链接：https://arxiv.org/abs/2401.10213
• MobileNetV3代码：torchvision.models
• Euro NCAP协议：https://www.euroncap.com/safe-driving/

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总结

MobileNet疲劳分心检测关键成果：

维度	性能
疲劳检测准确率	94.8%
分心检测准确率	91.5%
推理速度	45fps (CPU)
模型大小	2.5MB

IMS开发优先级：

• 🔴 高：MobileNetV3模型集成
• 🔴 高：多任务训练流程
• 🟡 中：INT8量化部署
• 🟢 低：模型蒸馏优化（可选）

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2026-07-11 研究笔记 | arXiv 2024