FatigueNet：GNN+Transformer混合架构实时多模态疲劳检测（Nature 2025）

FatigueNet：GNN+Transformer混合架构实时多模态疲劳检测

论文来源： Nature Scientific Reports
期刊： Scientific Reports, September 30, 2025
核心创新： 图神经网络 + Transformer 混合架构 → 多模态融合疲劳检测

---

论文信息

项目	内容
标题	FatigueNet: A hybrid graph neural network and transformer framework for real-time multimodal fatigue detection
期刊	Nature Scientific Reports
日期	September 30, 2025
创新	GNN处理结构化数据 + Transformer处理时序数据

---

核心问题：多模态疲劳检测

单一模态局限：

仅视觉：光照、遮挡影响大
仅生理信号：传感器不便佩戴
仅车辆行为：个体差异大

多模态融合优势：
- 视觉 + 生理 + 车辆 = 互补增强
- 融合精度 > 单一模态
- Euro NCAP 2026推荐多模态

---

FatigueNet架构

GNN + Transformer 混合设计

import torch
import torch.nn as nn
import torch.nn.functional as F

class FatigueNet(nn.Module):
    """
    FatigueNet: GNN + Transformer 混合架构
    
    Nature 2025 论文核心：
    1. GNN处理图结构数据（人脸关键点图）
    2. Transformer处理时序数据（生理信号、车辆数据）
    3. 多模态融合决策
    """
    
    def __init__(self,
                 num_landmarks: int = 68,  # 人脸关键点数
                 landmark_dim: int = 2,    # 每个关键点维度
                 physio_dim: int = 4,      # 生理信号维度（心率、皮肤电导等）
                 vehicle_dim: int = 6,     # 车辆数据维度
                 hidden_dim: int = 256,
                 num_gnn_layers: int = 3,
                 num_transformer_layers: int = 4):
        super().__init__()
        
        # GNN分支：处理人脸关键点图
        self.gnn_branch = GNNEncoder(
            num_landmarks=num_landmarks,
            input_dim=landmark_dim,
            hidden_dim=hidden_dim,
            num_layers=num_gnn_layers
        )
        
        # Transformer分支1：处理生理信号时序
        self.physio_transformer = TransformerEncoder(
            input_dim=physio_dim,
            hidden_dim=hidden_dim,
            num_layers=num_transformer_layers
        )
        
        # Transformer分支2：处理车辆数据时序
        self.vehicle_transformer = TransformerEncoder(
            input_dim=vehicle_dim,
            hidden_dim=hidden_dim,
            num_layers=num_transformer_layers
        )
        
        # 多模态融合层
        self.fusion = nn.Sequential(
            nn.Linear(hidden_dim * 3, hidden_dim),
            nn.ReLU(inplace=True),
            nn.Dropout(0.3),
            nn.Linear(hidden_dim, 128),
            nn.ReLU(inplace=True)
        )
        
        # 疲劳等级分类头（0-4级）
        self.classifier = nn.Linear(128, 5)  # 5个疲劳等级
    
    def forward(self, 
                landmarks: torch.Tensor,
                physio_signal: torch.Tensor,
                vehicle_data: torch.Tensor) -> torch.Tensor:
        """
        前向传播
        
        Args:
            landmarks: 人脸关键点, shape=(B, num_landmarks, 2)
            physio_signal: 生理信号时序, shape=(B, T, physio_dim)
            vehicle_data: 车辆数据时序, shape=(B, T, vehicle_dim)
        
        Returns:
            logits: 疲劳等级分类, shape=(B, 5)
        """
        # GNN处理关键点
        gnn_features = self.gnn_branch(landmarks)  # (B, hidden_dim)
        
        # Transformer处理生理信号
        physio_features = self.physio_transformer(physio_signal)  # (B, hidden_dim)
        
        # Transformer处理车辆数据
        vehicle_features = self.vehicle_transformer(vehicle_data)  # (B, hidden_dim)
        
        # 多模态融合
        fused = torch.cat([gnn_features, physio_features, vehicle_features], dim=1)
        fused_features = self.fusion(fused)
        
        # 分类
        logits = self.classifier(fused_features)
        
        return logits


class GNNEncoder(nn.Module):
    """
    图神经网络编码器
    
    处理人脸关键点图结构
    """
    
    def __init__(self, 
                 num_landmarks: int,
                 input_dim: int,
                 hidden_dim: int,
                 num_layers: int):
        super().__init__()
        
        # 节点嵌入
        self.node_embed = nn.Linear(input_dim, hidden_dim)
        
        # GNN层（简化版GCN）
        self.gnn_layers = nn.ModuleList([
            GCNConv(hidden_dim, hidden_dim)
            for _ in range(num_layers)
        ])
        
        # 人脸关键点邻接矩阵（固定结构）
        self.adj = self._build_face_adjacency(num_landmarks)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: 关键点坐标, shape=(B, N, 2)
        
        Returns:
            graph_features: shape=(B, hidden_dim)
        """
        B, N, _ = x.shape
        
        # 节点嵌入
        x = self.node_embed(x)  # (B, N, hidden_dim)
        
        # GNN传播
        for layer in self.gnn_layers:
            x = layer(x, self.adj)
            x = F.relu(x)
        
        # 图池化（全局平均）
        graph_features = x.mean(dim=1)  # (B, hidden_dim)
        
        return graph_features
    
    def _build_face_adjacency(self, num_landmarks: int) -> torch.Tensor:
        """构建人脸关键点邻接矩阵"""
        # 简化：假设相邻关键点连接
        adj = torch.zeros(num_landmarks, num_landmarks)
        
        # 左眼连接（点36-41）
        for i in range(36, 41):
            adj[i, i+1] = 1
            adj[i+1, i] = 1
        
        # 右眼连接（点42-47）
        for i in range(42, 47):
            adj[i, i+1] = 1
            adj[i+1, i] = 1
        
        # 嘴巴连接（点48-67）
        for i in range(48, 67):
            adj[i, i+1] = 1
            adj[i+1, i] = 1
        
        # 添加自环
        adj = adj + torch.eye(num_landmarks)
        
        # 归一化
        degree = adj.sum(dim=1, keepdim=True)
        adj_norm = adj / degree
        
        return adj_norm


class GCNConv(nn.Module):
    """简化版图卷积"""
    
    def __init__(self, in_features: int, out_features: int):
        super().__init__()
        self.linear = nn.Linear(in_features, out_features)
    
    def forward(self, x: torch.Tensor, adj: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: 节点特征, shape=(B, N, F)
            adj: 邻接矩阵, shape=(N, N)
        
        Returns:
            out: shape=(B, N, F')
        """
        # 图卷积：AXW
        x = torch.matmul(adj, x)  # (B, N, F)
        x = self.linear(x)  # (B, N, F')
        return x


class TransformerEncoder(nn.Module):
    """Transformer编码器"""
    
    def __init__(self, 
                 input_dim: int,
                 hidden_dim: int,
                 num_layers: int = 4,
                 num_heads: int = 8):
        super().__init__()
        
        # 输入嵌入
        self.input_embed = nn.Linear(input_dim, hidden_dim)
        
        # Transformer层
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=hidden_dim,
            nhead=num_heads,
            dim_feedforward=hidden_dim * 4,
            dropout=0.1,
            batch_first=True
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: 时序数据, shape=(B, T, input_dim)
        
        Returns:
            features: shape=(B, hidden_dim)
        """
        # 嵌入
        x = self.input_embed(x)  # (B, T, hidden_dim)
        
        # Transformer编码
        x = self.transformer(x)  # (B, T, hidden_dim)
        
        # 时序池化
        features = x.mean(dim=1)  # (B, hidden_dim)
        
        return features


# 测试示例
if __name__ == "__main__":
    model = FatigueNet()
    
    # 模拟输入
    B, T = 2, 30  # 2个样本，30帧
    
    landmarks = torch.randn(B, 68, 2)  # 68个人脸关键点
    physio_signal = torch.randn(B, T, 4)  # 生理信号（心率等）
    vehicle_data = torch.randn(B, T, 6)  # 车辆数据（速度、方向盘等）
    
    output = model(landmarks, physio_signal, vehicle_data)
    print(f"输入关键点形状: {landmarks.shape}")
    print(f"输入生理信号形状: {physio_signal.shape}")
    print(f"输入车辆数据形状: {vehicle_data.shape}")
    print(f"输出疲劳等级: {output.shape}")
    print(f"疲劳等级概率: {F.softmax(output, dim=1)}")

---

多模态数据融合策略

数据源与特征

模态	输入数据	特征提取	维度
视觉	人脸关键点	GNN	68×2
生理	心率、皮肤电导、体温	Transformer	4维时序
车辆	速度、方向盘、车道偏移	Transformer	6维时序

融合权重

class AdaptiveFusion(nn.Module):
    """
    自适应多模态融合
    
    动态调整各模态权重
    """
    
    def __init__(self, num_modalities: int = 3):
        super().__init__()
        
        # 模态权重预测
        self.weight_net = nn.Sequential(
            nn.Linear(num_modalities * 256, 128),
            nn.ReLU(),
            nn.Linear(128, num_modalities)
        )
    
    def forward(self, *modality_features) -> torch.Tensor:
        """
        自适应融合
        
        Args:
            modality_features: 各模态特征
        
        Returns:
            fused_features: 融合特征
        """
        # 计算模态权重
        concat_features = torch.cat(modality_features, dim=1)
        weights = F.softmax(self.weight_net(concat_features), dim=1)
        
        # 加权融合
        stacked = torch.stack(modality_features, dim=1)  # (B, M, D)
        fused = (stacked * weights.unsqueeze(2)).sum(dim=1)
        
        return fused, weights

---

性能评估（论文数据）

指标	单视觉	单生理	单车辆	FatigueNet融合
准确率	92.3%	88.5%	85.1%	98.7%
召回率	90.1%	86.2%	82.3%	98.2%
F1分数	91.2%	87.3%	83.7%	98.5%

---

Euro NCAP 2026多模态要求

场景	推荐模态组合	FatigueNet支持
F-01 PERCLOS	视觉	✅ GNN
F-02 闭眼检测	视觉	✅ GNN
F-03 打哈欠	视觉	✅ GNN
F-04 微睡眠	视觉+生理	✅ 融合
F-05 头部下垂	视觉+车辆	✅ 融合

---

IMS开发启示

传感器配置

传感器	数据类型	FatigueNet模块
IR摄像头	人脸关键点	GNN
心率带/耳EEG	生理信号	Transformer
CAN总线	车辆数据	Transformer

部署优化

class EdgeFatigueNet(nn.Module):
    """
    边缘优化FatigueNet
    
    优化策略：
    1. 轻量化GNN（减少层数）
    2. INT8量化
    3. 早退机制（低疲劳时跳过部分层）
    """
    
    def __init__(self, config: dict = None):
        super().__init__()
        
        # 轻量化配置
        self.gnn = GNNEncoder(68, 2, 128, num_layers=2)  # 减少层数
        self.physio_encoder = TransformerEncoder(4, 128, num_layers=2)
        self.vehicle_encoder = TransformerEncoder(6, 128, num_layers=2)
        
        self.classifier = nn.Linear(128 * 3, 5)
    
    def forward_early_exit(self, 
                           landmarks: torch.Tensor,
                           threshold: float = 0.7) -> dict:
        """
        早退机制
        
        如果GNN分支已经高置信度判断，跳过其他分支
        """
        # 快速GNN判断
        gnn_features = self.gnn(landmarks)
        early_logits = self.classifier(torch.cat([gnn_features, 
                                                    torch.zeros_like(gnn_features),
                                                    torch.zeros_like(gnn_features)], dim=1))
        
        early_prob = F.softmax(early_logits, dim=1)
        early_conf = early_prob.max(dim=1)[0]
        
        # 早退判断
        if early_conf > threshold:
            return {
                "logits": early_logits,
                "early_exit": True,
                "confidence": early_conf
            }
        
        # 完整推理（需要更多数据）
        return {
            "logits": None,
            "early_exit": False,
            "confidence": early_conf
        }

---

参考资料

1. Nature Scientific Reports - FatigueNet
2. Graph Neural Networks for Face Analysis
3. Transformer for Multimodal Fusion
4. Euro NCAP 2026 Multimodal Recommendations

---

总结

FatigueNet核心：

1. GNN处理结构化图：人脸关键点拓扑
2. Transformer处理时序：生理/车辆信号
3. 多模态融合：98.7%准确率

IMS开发优先级：

• 🔴 高：GNN人脸关键点模块
• 🟡 中：多模态数据融合
• 🟢 低：早退机制优化

下一步行动：

• 实现GNNEncoder模块
• 集成多模态数据采集
• 对齐Euro NCAP F-04/F-05场景