TDGH-YOLOv7：基于 Transformer 的驾驶员头部姿态与眼动检测模型

模型背景

传统方法的局限

传统的驾驶员头部姿态和眼动检测方法存在以下问题：

方法	局限性
传统 CNN	难以精确定位小目标（眼睛）
单独训练	头部姿态和眼动分开训练，缺乏协同
固定输入	对不同尺寸目标适应性差

TDGH-YOLOv7 创新点

TDGH（Transformer Detection of Gaze and Head）集成到 YOLOv7 架构：

创新点：
├─ Transformer 机制增强小目标检测
├─ 头部姿态和眼动联合检测
├─ 实时性能（>30 FPS）
└─ 鲁棒性（遮挡、光照变化）

模型架构

整体结构

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

class TDGHYOLOv7(nn.Module):
    """
    TDGH-YOLOv7: Transformer Detection of Gaze and Head
    
    基于 YOLOv7 的驾驶员头部姿态和眼动检测模型
    
    参考：
    "AI-enabled driver assistance: monitoring head and gaze movements 
     for enhanced safety" Complex & Intelligent Systems, 2025
    """
    
    def __init__(self, 
                 num_head_poses: int = 9,  # 9 个头部姿态类别
                 num_gaze_directions: int = 9):  # 9 个注视方向
        super().__init__()
        
        # Backbone: YOLOv7 的 E-ELAN 结构
        self.backbone = self._build_backbone()
        
        # Neck: PAN + Transformer
        self.neck = self._build_neck()
        
        # Head: 多任务检测头
        self.head = self._build_head(num_head_poses, num_gaze_directions)
        
    def _build_backbone(self) -> nn.Module:
        """构建 Backbone"""
        # 简化实现：使用标准的 CNN 结构
        layers = []
        
        # Stem
        layers.extend([
            nn.Conv2d(3, 32, 3, stride=1, padding=1),
            nn.BatchNorm2d(32),
            nn.SiLU(),
            nn.Conv2d(32, 64, 3, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.SiLU(),
        ])
        
        # Stage 1
        layers.extend([
            self._make_elan_block(64, 64, expand_ratio=2),
            nn.Conv2d(128, 128, 3, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.SiLU(),
        ])
        
        # Stage 2
        layers.extend([
            self._make_elan_block(128, 128, expand_ratio=2),
            nn.Conv2d(256, 256, 3, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.SiLU(),
        ])
        
        # Stage 3
        layers.extend([
            self._make_elan_block(256, 256, expand_ratio=2),
        ])
        
        return nn.Sequential(*layers)
    
    def _make_elan_block(self, in_channels: int, 
                         out_channels: int, 
                         expand_ratio: int = 2) -> nn.Module:
        """构建 E-ELAN 模块"""
        hidden_channels = in_channels * expand_ratio
        
        return nn.Sequential(
            nn.Conv2d(in_channels, hidden_channels, 1),
            nn.BatchNorm2d(hidden_channels),
            nn.SiLU(),
            nn.Conv2d(hidden_channels, hidden_channels, 3, padding=1),
            nn.BatchNorm2d(hidden_channels),
            nn.SiLU(),
            nn.Conv2d(hidden_channels, out_channels, 1),
            nn.BatchNorm2d(out_channels),
            nn.SiLU(),
        )
    
    def _build_neck(self) -> nn.Module:
        """构建 Neck（包含 Transformer）"""
        return TransformerNeck(
            in_channels=512,
            hidden_dim=256,
            num_heads=8,
            num_layers=3
        )
    
    def _build_head(self, num_poses: int, num_gazes: int) -> nn.Module:
        """构建多任务检测头"""
        return MultiTaskHead(
            in_channels=256,
            num_head_poses=num_poses,
            num_gaze_directions=num_gazes
        )
    
    def forward(self, x: torch.Tensor) -> dict:
        """
        前向传播
        
        Args:
            x: 输入图像, shape=(batch, 3, H, W)
            
        Returns:
            outputs: 检测结果
        """
        # Backbone
        features = self.backbone(x)
        
        # Neck
        enhanced_features = self.neck(features)
        
        # Head
        outputs = self.head(enhanced_features)
        
        return outputs


class TransformerNeck(nn.Module):
    """
    Transformer Neck
    
    使用 Transformer 增强特征提取能力，特别是小目标（眼睛）检测
    """
    
    def __init__(self, 
                 in_channels: int,
                 hidden_dim: int,
                 num_heads: int,
                 num_layers: int):
        super().__init__()
        
        self.in_channels = in_channels
        self.hidden_dim = hidden_dim
        
        # 输入投影
        self.input_proj = nn.Conv2d(in_channels, hidden_dim, 1)
        
        # Transformer Encoder
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=hidden_dim,
            nhead=num_heads,
            dim_feedforward=hidden_dim * 4,
            dropout=0.1,
            activation='gelu',
            batch_first=True
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
        
        # 输出投影
        self.output_proj = nn.Conv2d(hidden_dim, hidden_dim, 1)
        
        # BPFE (Binary Pattern Feature Extraction)
        self.bpfe = BPFE(hidden_dim)
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        前向传播
        
        Args:
            x: 输入特征, shape=(batch, C, H, W)
            
        Returns:
            output: 增强特征
        """
        batch, C, H, W = x.shape
        
        # 投影
        x = self.input_proj(x)  # (batch, hidden_dim, H, W)
        
        # 展平为序列
        x_flat = x.flatten(2).transpose(1, 2)  # (batch, H*W, hidden_dim)
        
        # Transformer
        x_transformed = self.transformer(x_flat)
        
        # 恢复形状
        x_out = x_transformed.transpose(1, 2).reshape(batch, self.hidden_dim, H, W)
        
        # BPFE
        x_out = self.bpfe(x_out)
        
        # 输出投影
        x_out = self.output_proj(x_out)
        
        return x_out


class BPFE(nn.Module):
    """
    Binary Pattern Feature Extraction
    
    提取纹理特征用于面部区域检测
    """
    
    def __init__(self, channels: int):
        super().__init__()
        
        self.conv1 = nn.Conv2d(channels, channels, 3, padding=1)
        self.conv2 = nn.Conv2d(channels, channels, 3, padding=1)
        self.bn = nn.BatchNorm2d(channels)
        self.act = nn.SiLU()
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # 二进制模式提取
        x1 = self.conv1(x)
        x2 = self.conv2(x)
        
        # 融合
        x_out = x + self.act(self.bn(x1 + x2))
        
        return x_out


class MultiTaskHead(nn.Module):
    """
    多任务检测头
    
    同时检测：
    - 人脸边界框
    - 头部姿态类别
    - 眼对边界框
    - 注视方向类别
    """
    
    def __init__(self, 
                 in_channels: int,
                 num_head_poses: int,
                 num_gaze_directions: int):
        super().__init__()
        
        # 共享卷积
        self.shared_conv = nn.Sequential(
            nn.Conv2d(in_channels, 128, 3, padding=1),
            nn.BatchNorm2d(128),
            nn.SiLU(),
        )
        
        # 人脸检测分支
        self.face_det = nn.Conv2d(128, 5, 1)  # (x, y, w, h, conf)
        
        # 头部姿态分类分支
        self.head_pose_cls = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, num_head_poses)
        )
        
        # 眼对检测分支
        self.eye_det = nn.Conv2d(128, 10, 1)  # 左眼(x,y,w,h,conf) + 右眼(x,y,w,h,conf)
        
        # 注视方向分类分支
        self.gaze_cls = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, num_gaze_directions)
        )
        
    def forward(self, x: torch.Tensor) -> dict:
        """
        前向传播
        
        Args:
            x: 输入特征, shape=(batch, C, H, W)
            
        Returns:
            outputs: 多任务输出
        """
        # 共享特征
        shared = self.shared_conv(x)
        
        # 各任务输出
        face_bbox = self.face_det(shared)
        head_pose = self.head_pose_cls(shared)
        eye_bbox = self.eye_det(shared)
        gaze_dir = self.gaze_cls(shared)
        
        return {
            'face_bbox': face_bbox,
            'head_pose': head_pose,
            'eye_bbox': eye_bbox,
            'gaze_direction': gaze_dir
        }

头部姿态分类定义

9 类头部姿态

头部姿态定义（俯仰角 × 偏航角）：

            向上
              ↑
              │
              │
向左 ←───── 正常 ─────→ 向右
              │
              │
              ↓
            向下

组合 9 类：
┌───────┬───────┬───────┐
│左上   │ 正上  │ 右上  │
├───────┼───────┼───────┤
│ 向左  │ 正常  │ 向右  │
├───────┼───────┼───────┤
│左下   │ 正下  │ 右下  │
└───────┴───────┴───────┘

注视方向分类

# 注视方向定义（与头部姿态类似，但更精细）
class GazeDirection:
    """
    注视方向枚举
    
    定义 9 个注视区域
    """
    
    DIRECTIONS = {
        0: 'center',       # 中央（道路）
        1: 'up',           # 向上
        2: 'down',         # 向下
        3: 'left',         # 向左
        4: 'right',        # 向右
        5: 'up_left',      # 左上
        6: 'up_right',     # 右上
        7: 'down_left',    # 左下
        8: 'down_right'    # 右下
    }
    
    # 对应 Euro NCAP 注视区域
    EURO_NCAP_MAPPING = {
        'center': 'road_forward',
        'left': 'driver_side_mirror',
        'right': 'passenger_side_mirror',
        'up': 'rear_mirror',
        'down': 'instrument_cluster',
        'down_left': 'driver_lap',
        'down_right': 'center_stack'
    }

训练与部署

数据集构建

class FaceEyeDataset(torch.utils.data.Dataset):
    """
    面部与眼动数据集
    
    数据格式：
    - 图像：驾驶员驾驶时的面部图像
    - 标注：人脸框、眼对框、头部姿态、注视方向
    """
    
    def __init__(self, 
                 image_dir: str,
                 annotation_file: str,
                 transform=None):
        self.image_dir = image_dir
        self.annotations = self._load_annotations(annotation_file)
        self.transform = transform
        
    def __len__(self):
        return len(self.annotations)
    
    def __getitem__(self, idx):
        ann = self.annotations[idx]
        
        # 加载图像
        image = cv2.imread(ann['image_path'])
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        
        # 标注
        target = {
            'face_bbox': torch.tensor(ann['face_bbox'], dtype=torch.float32),
            'head_pose': torch.tensor(ann['head_pose'], dtype=torch.long),
            'eye_left_bbox': torch.tensor(ann['eye_left_bbox'], dtype=torch.float32),
            'eye_right_bbox': torch.tensor(ann['eye_right_bbox'], dtype=torch.float32),
            'gaze_direction': torch.tensor(ann['gaze_direction'], dtype=torch.long)
        }
        
        if self.transform:
            image = self.transform(image)
        
        return image, target
    
    def _load_annotations(self, file_path):
        # 加载标注文件（JSON/CSV 格式）
        import json
        with open(file_path, 'r') as f:
            return json.load(f)

训练代码

def train_model():
    """训练 TDGH-YOLOv7 模型"""
    
    # 数据加载
    train_dataset = FaceEyeDataset(
        image_dir='data/train/images',
        annotation_file='data/train/annotations.json'
    )
    
    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=16,
        shuffle=True,
        num_workers=4
    )
    
    # 模型
    model = TDGHYOLOv7(num_head_poses=9, num_gaze_directions=9)
    model = model.cuda()
    
    # 损失函数
    bbox_loss = nn.SmoothL1Loss()
    cls_loss = nn.CrossEntropyLoss()
    
    # 优化器
    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100)
    
    # 训练循环
    for epoch in range(100):
        model.train()
        total_loss = 0
        
        for batch_idx, (images, targets) in enumerate(train_loader):
            images = images.cuda()
            
            # 前向传播
            outputs = model(images)
            
            # 计算损失
            loss_face = bbox_loss(outputs['face_bbox'], targets['face_bbox'].cuda())
            loss_head_pose = cls_loss(outputs['head_pose'], targets['head_pose'].cuda())
            loss_eye = bbox_loss(outputs['eye_bbox'], targets['eye_bbox'].cuda())
            loss_gaze = cls_loss(outputs['gaze_direction'], targets['gaze_direction'].cuda())
            
            loss = loss_face + loss_head_pose + loss_eye + loss_gaze
            
            # 反向传播
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
            total_loss += loss.item()
        
        scheduler.step()
        
        print(f"Epoch {epoch+1}, Loss: {total_loss/len(train_loader):.4f}")
        
        # 验证
        if (epoch + 1) % 10 == 0:
            validate(model)
    
    # 保存模型
    torch.save(model.state_dict(), 'tdgh_yolov7.pth')


def validate(model):
    """验证模型"""
    model.eval()
    # 实现验证逻辑
    pass

性能指标

实验结果

任务	准确率	帧率
人脸检测	99.2%	45 FPS
头部姿态分类	95.4%	45 FPS
眼动检测	97.1%	45 FPS
注视方向分类	93.8%	45 FPS

与 Baseline 对比

模型	头部姿态准确率	注视方向准确率	帧率
ResNet-50	89.3%	85.2%	30 FPS
YOLOv5	91.5%	87.6%	40 FPS
TDGH-YOLOv7	95.4%	93.8%	45 FPS

IMS 开发启示

1. 模型选型

场景	推荐模型	原因
实时性优先	TDGH-YOLOv7	45 FPS 高帧率
精度优先	HRNet + Transformer	更高精度
嵌入式部署	MobileNetV3 + 轻量 Transformer	低功耗

2. 部署优化

# ONNX 导出
def export_to_onnx(model, output_path):
    """导出为 ONNX 格式"""
    model.eval()
    
    dummy_input = torch.randn(1, 3, 640, 640)
    
    torch.onnx.export(
        model,
        dummy_input,
        output_path,
        opset_version=11,
        input_names=['image'],
        output_names=['face_bbox', 'head_pose', 'eye_bbox', 'gaze_direction'],
        dynamic_axes={
            'image': {0: 'batch_size'},
            'face_bbox': {0: 'batch_size'},
            'head_pose': {0: 'batch_size'},
            'eye_bbox': {0: 'batch_size'},
            'gaze_direction': {0: 'batch_size'}
        }
    )
    
    print(f"Model exported to {output_path}")


# TensorRT 优化
def optimize_with_tensorrt(onnx_path, engine_path):
    """使用 TensorRT 优化"""
    import tensorrt as trt
    
    logger = trt.Logger(trt.Logger.WARNING)
    builder = trt.Builder(logger)
    
    network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
    parser = trt.OnnxParser(network, logger)
    
    with open(onnx_path, 'rb') as f:
        parser.parse(f.read())
    
    config = builder.create_builder_config()
    config.max_workspace_size = 1 << 30  # 1GB
    config.set_flag(trt.BuilderFlag.FP16)  # FP16 精度
    
    engine = builder.build_engine(network, config)
    
    with open(engine_path, 'wb') as f:
        f.write(engine.serialize())
    
    print(f"TensorRT engine saved to {engine_path}")

3. 边缘设备性能

设备	帧率 (FP16)	功耗
Jetson Orin NX	35 FPS	15W
Jetson Xavier NX	25 FPS	15W
QCS8255	30 FPS	10W
Intel Movidius	15 FPS	2W

---

参考来源：

• TDGH-YOLOv7 Paper
• YOLOv7 Official
• Vision Transformer