Transformer在DMS中的应用：Swin Transformer驾驶员行为检测

Transformer在DMS中的应用：Swin Transformer驾驶员行为检测

论文信息

  • 标题： A Driver Behavior Detection Model for Human-Machine Co-Driving Systems Based on an Improved Swin Transformer
  • 期刊： World Electric Vehicle Journal (MDPI)
  • 发表时间： 2024年12月27日
  • 开源状态： Open Access

核心创新

该论文提出改进的Swin Transformer用于驾驶员行为检测，核心创新点：

  1. ECA注意力模块：在自注意力后添加高效通道注意力
  2. 多尺度特征融合：捕捉不同粒度的行为特征
  3. 轻量化设计：适配边缘设备部署

方法详解

1. Swin Transformer基础

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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

class WindowAttention(nn.Module):
    """
    Swin Transformer窗口注意力
    
    论文核心组件：局部窗口内的自注意力计算
    """
    
    def __init__(self, dim: int, window_size: int, num_heads: int):
        super().__init__()
        self.dim = dim
        self.window_size = window_size
        self.num_heads = num_heads
        
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)
        
        # 相对位置编码
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), num_heads)
        )
        
        nn.init.trunc_normal_(self.relative_position_bias_table, std=0.02)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: 输入特征, shape=(B*num_windows, window_size*window_size, C)
        Returns:
            注意力输出
        """
        B_, N, C = x.shape
        
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads)
        qkv = qkv.permute(2, 0, 3, 1, 4)  # (3, B_, heads, N, C//heads)
        q, k, v = qkv[0], qkv[1], qkv[2]
        
        q = q * (C // self.num_heads) ** -0.5
        
        # 注意力计算
        attn = (q @ k.transpose(-2, -1))
        
        # 添加相对位置偏置
        attn = attn + self._get_relative_position_bias()
        
        attn = F.softmax(attn, dim=-1)
        
        # 输出
        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        
        return x
    
    def _get_relative_position_bias(self) -> torch.Tensor:
        """获取相对位置偏置"""
        # 简化实现
        return torch.zeros(
            self.num_heads, 
            self.window_size ** 2, 
            self.window_size ** 2,
            device=self.relative_position_bias_table.device
        )


class SwinTransformerBlock(nn.Module):
    """
    Swin Transformer块
    
    包含：窗口注意力 + MLP + 残差连接
    """
    
    def __init__(self, dim: int, num_heads: int, window_size: int = 7,
                 mlp_ratio: float = 4.0, dropout: float = 0.0):
        super().__init__()
        
        self.norm1 = nn.LayerNorm(dim)
        self.attn = WindowAttention(dim, window_size, num_heads)
        
        self.norm2 = nn.LayerNorm(dim)
        self.mlp = nn.Sequential(
            nn.Linear(dim, int(dim * mlp_ratio)),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(int(dim * mlp_ratio), dim),
            nn.Dropout(dropout)
        )
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """前向传播"""
        shortcut = x
        x = self.norm1(x)
        x = self.attn(x)
        x = shortcut + x
        
        x = x + self.mlp(self.norm2(x))
        
        return x

2. ECA注意力模块

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class ECALayer(nn.Module):
    """
    Efficient Channel Attention (ECA) 模块
    
    论文核心改进：在Swin Transformer后添加通道注意力
    优势：极低参数量，显著提升性能
    """
    
    def __init__(self, channels: int, gamma: int = 2, b: int = 1):
        super().__init__()
        
        # 自适应kernel大小计算
        k_size = self._calc_kernel_size(channels, gamma, b)
        
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv = nn.Conv1d(1, 1, kernel_size=k_size, 
                              padding=(k_size - 1) // 2, bias=False)
        self.sigmoid = nn.Sigmoid()
    
    def _calc_kernel_size(self, channels: int, gamma: int, b: int) -> int:
        """计算自适应kernel大小"""
        t = int(abs((np.log2(channels) + b) / gamma))
        k = t if t % 2 else t + 1
        return k
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: 输入特征, shape=(B, C, H, W)
        Returns:
            通道加权后的特征
        """
        # 全局平均池化
        y = self.avg_pool(x)  # (B, C, 1, 1)
        
        # 1D卷积
        y = self.conv(y.squeeze(-1).transpose(-1, -2))  # (B, 1, C)
        y = y.transpose(-1, -2).unsqueeze(-1)  # (B, C, 1, 1)
        
        # Sigmoid激活
        y = self.sigmoid(y)
        
        # 通道加权
        return x * y.expand_as(x)


class ImprovedSwinBlock(nn.Module):
    """
    改进的Swin Transformer块
    
    添加ECA模块增强通道注意力
    """
    
    def __init__(self, dim: int, num_heads: int, window_size: int = 7):
        super().__init__()
        
        self.swin_block = SwinTransformerBlock(dim, num_heads, window_size)
        self.eca = ECALayer(dim)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: 输入特征, shape=(B, H*W, C) for Swin
        Returns:
            增强后的特征
        """
        # Swin Transformer
        x = self.swin_block(x)
        
        # 转换为图像格式应用ECA
        B, N, C = x.shape
        H = W = int(np.sqrt(N))
        x_img = x.transpose(1, 2).reshape(B, C, H, W)
        
        # ECA
        x_img = self.eca(x_img)
        
        # 转回序列格式
        x = x_img.reshape(B, C, -1).transpose(1, 2)
        
        return x

3. 完整网络架构

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class DriverBehaviorSwinTransformer(nn.Module):
    """
    驾驶员行为检测Swin Transformer
    
    论文架构：
    - Patch Partition -> Stage 1-4 -> Head
    
    行为类别：
    - 正常驾驶
    - 打电话 (左/右手)
    - 发短信 (左/右手)
    - 调整收音机
    - 喝水
    - 伸手后座
    - 整理头发
    - 与乘客交谈
    """
    
    def __init__(self, img_size: int = 224, patch_size: int = 4,
                 in_channels: int = 3, num_classes: int = 10,
                 embed_dim: int = 96, depths: list = [2, 2, 6, 2],
                 num_heads: list = [3, 6, 12, 24]):
        super().__init__()
        
        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        
        # Patch Partition
        self.patch_embed = nn.Sequential(
            nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, 
                     stride=patch_size),
            nn.LayerNorm(embed_dim)
        )
        
        # Stages
        self.stages = nn.ModuleList()
        for i_layer in range(self.num_layers):
            stage = self._make_stage(
                dim=int(embed_dim * 2 ** i_layer),
                depth=depths[i_layer],
                num_heads=num_heads[i_layer]
            )
            self.stages.append(stage)
        
        # Classification Head
        self.norm = nn.LayerNorm(int(embed_dim * 2 ** (self.num_layers - 1)))
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.head = nn.Linear(
            int(embed_dim * 2 ** (self.num_layers - 1)),
            num_classes
        )
    
    def _make_stage(self, dim: int, depth: int, num_heads: int) -> nn.Sequential:
        """构建一个Stage"""
        blocks = []
        for _ in range(depth):
            blocks.append(ImprovedSwinBlock(dim, num_heads))
        
        # 下采样
        blocks.append(nn.LayerNorm(dim))
        blocks.append(PatchMerging(dim))
        
        return nn.Sequential(*blocks)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        前向传播
        
        Args:
            x: 输入图像, shape=(B, 3, 224, 224)
        Returns:
            分类logits, shape=(B, num_classes)
        """
        # Patch Embedding
        x = self.patch_embed(x)  # (B, H/4, W/4, C)
        
        # Flatten
        B, H, W, C = x.shape
        x = x.flatten(1, 2)  # (B, H*W/16, C)
        
        # Stages
        for stage in self.stages:
            x = stage(x)
        
        # Output
        x = self.norm(x)
        x = x.transpose(1, 2)  # (B, C, N)
        x = self.avgpool(x).flatten(1)  # (B, C)
        x = self.head(x)
        
        return x


class PatchMerging(nn.Module):
    """Patch合并 (下采样)"""
    
    def __init__(self, dim: int):
        super().__init__()
        self.norm = nn.LayerNorm(4 * dim)
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: (B, H*W, C)
        Returns:
            (B, H*W/4, 2*C)
        """
        B, L, C = x.shape
        H = W = int(np.sqrt(L))
        
        x = x.reshape(B, H, W, C)
        
        # 合并2x2 patches
        x0 = x[:, 0::2, 0::2, :]  # B, H/2, W/2, C
        x1 = x[:, 1::2, 0::2, :]
        x2 = x[:, 0::2, 1::2, :]
        x3 = x[:, 1::2, 1::2, :]
        
        x = torch.cat([x0, x1, x2, x3], dim=-1)  # B, H/2, W/2, 4*C
        x = x.flatten(1, 2)  # B, H*W/4, 4*C
        
        x = self.norm(x)
        x = self.reduction(x)
        
        return x

训练与部署

1. 训练代码

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import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

def train_driver_behavior_model():
    """
    训练驾驶员行为检测模型
    
    论文配置：
    - Optimizer: AdamW
    - LR: 1e-4
    - Weight Decay: 0.05
    - Epochs: 100
    - Batch Size: 64
    """
    
    # 数据预处理
    train_transform = transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.RandomCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ColorJitter(brightness=0.4, contrast=0.4, 
                              saturation=0.4, hue=0.1),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                           std=[0.229, 0.224, 0.225])
    ])
    
    # 加载数据
    train_dataset = datasets.ImageFolder('data/train', transform=train_transform)
    train_loader = DataLoader(train_dataset, batch_size=64, 
                              shuffle=True, num_workers=4)
    
    # 模型
    model = DriverBehaviorSwinTransformer(
        img_size=224,
        num_classes=10,
        embed_dim=96,
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24]
    )
    
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = model.to(device)
    
    # 优化器
    optimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=0.05)
    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100)
    criterion = nn.CrossEntropyLoss()
    
    # 训练循环
    for epoch in range(100):
        model.train()
        total_loss = 0
        correct = 0
        total = 0
        
        for images, labels in train_loader:
            images, labels = images.to(device), labels.to(device)
            
            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            
            total_loss += loss.item()
            _, predicted = outputs.max(1)
            total += labels.size(0)
            correct += predicted.eq(labels).sum().item()
        
        scheduler.step()
        
        acc = 100.0 * correct / total
        print(f"Epoch {epoch+1}/100 | Loss: {total_loss/len(train_loader):.4f} | "
              f"Acc: {acc:.2f}%")
    
    # 保存模型
    torch.save(model.state_dict(), 'swin_driver_behavior.pth')
    
    return model


if __name__ == "__main__":
    model = train_driver_behavior_model()

2. 量化部署

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def export_onnx_for_npu():
    """
    导出ONNX用于NPU部署
    
    Qualcomm QCS8255优化：
    - INT8量化
    - 动态batch
    - 优化计算图
    """
    import torch.onnx
    
    # 加载模型
    model = DriverBehaviorSwinTransformer()
    model.load_state_dict(torch.load('swin_driver_behavior.pth'))
    model.eval()
    
    # 导出ONNX
    dummy_input = torch.randn(1, 3, 224, 224)
    
    torch.onnx.export(
        model,
        dummy_input,
        'swin_driver_behavior.onnx',
        input_names=['image'],
        output_names=['logits'],
        dynamic_axes={
            'image': {0: 'batch_size'},
            'logits': {0: 'batch_size'}
        },
        opset_version=14
    )
    
    print("ONNX模型已导出: swin_driver_behavior.onnx")
    
    # 量化命令
    print("\n量化命令:")
    print("""
    # Qualcomm AI Hub 量化
    pip install qai_hub_models
    qai_hub_models.export_model(
        model='swin_driver_behavior.onnx',
        device='QCS8255',
        quantize=True
    )
    """)

性能对比

与传统CNN对比

模型 参数量 FLOPs 准确率 延迟(QCS8255)
ResNet50 25.6M 4.12G 95.8% 35ms
EfficientNet-B4 19.0M 4.20G 96.2% 32ms
ViT-Base 86.6M 17.6G 95.5% 85ms
Swin-T (本文) 28.3M 4.50G 97.1% 38ms
Swin-T+ECA 28.5M 4.52G 97.8% 40ms

消融实验

组件 准确率 说明
Baseline Swin-T 97.1% 无ECA
+ ECA (Stage 3) 97.4% 单阶段ECA
+ ECA (All Stages) 97.6% 全阶段ECA
+ 数据增强 97.8% 最终配置

IMS应用启示

1. 行为检测架构

graph LR
    A[IR摄像头] --> B[图像预处理]
    B --> C[Swin Transformer特征提取]
    C --> D[行为分类]
    D --> E{置信度判断}
    E -->|高分| F[直接决策]
    E -->|低分| G[时序平滑]
    F --> H[触发警告]
    G --> H

2. 部署优化建议

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# QCS8255部署配置
DEPLOYMENT_CONFIG = {
    'model': 'swin_driver_behavior_int8.onnx',
    'input_resolution': (224, 224),
    'batch_size': 1,
    'execution_provider': 'QNNExecutionProvider',
    'target_latency_ms': 50,
    'target_fps': 20,
    'power_budget_w': 5
}

3. 实时检测流程

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class RealTimeBehaviorDetector:
    """实时行为检测"""
    
    def __init__(self, model_path: str):
        import onnxruntime as ort
        
        self.session = ort.InferenceSession(
            model_path,
            providers=['QNNExecutionProvider']
        )
        
        # 行为标签
        self.behaviors = [
            'normal', 'phone_left', 'phone_right',
            'text_left', 'text_right', 'radio',
            'drinking', 'reaching', 'hair_makeup', 'talking'
        ]
        
        # 时序平滑
        self.history = []
        self.window_size = 5
    
    def detect(self, image: np.ndarray) -> dict:
        """
        检测行为
        
        Args:
            image: BGR图像
            
        Returns:
            {behavior, confidence, latency_ms}
        """
        import time
        
        # 预处理
        input_tensor = self._preprocess(image)
        
        # 推理
        start = time.perf_counter()
        outputs = self.session.run(None, {'image': input_tensor})
        latency = (time.perf_counter() - start) * 1000
        
        # 后处理
        probs = outputs[0][0]
        behavior_id = np.argmax(probs)
        confidence = probs[behavior_id]
        
        # 时序平滑
        self.history.append((behavior_id, confidence))
        if len(self.history) > self.window_size:
            self.history.pop(0)
        
        smoothed_id, smoothed_conf = self._smooth()
        
        return {
            'behavior': self.behaviors[smoothed_id],
            'confidence': float(smoothed_conf),
            'latency_ms': latency,
            'raw_behavior': self.behaviors[behavior_id]
        }
    
    def _preprocess(self, image: np.ndarray) -> np.ndarray:
        """预处理"""
        # BGR -> RGB
        image = image[:, :, ::-1]
        
        # Resize
        import cv2
        image = cv2.resize(image, (224, 224))
        
        # Normalize
        image = image.astype(np.float32) / 255.0
        mean = np.array([0.485, 0.456, 0.406])
        std = np.array([0.229, 0.224, 0.225])
        image = (image - mean) / std
        
        # HWC -> NCHW
        image = image.transpose(2, 0, 1)[np.newaxis, ...]
        
        return image
    
    def _smooth(self) -> tuple:
        """时序平滑"""
        if not self.history:
            return 0, 0.0
        
        # 投票
        from collections import Counter
        votes = Counter([h[0] for h in self.history])
        most_common = votes.most_common(1)[0][0]
        
        # 平均置信度
        relevant_confs = [h[1] for h in self.history if h[0] == most_common]
        avg_conf = np.mean(relevant_confs)
        
        return most_common, avg_conf

开发启示

1. 模型选择

场景 推荐模型 原因
高精度要求 Swin-T+ECA 最高准确率
低功耗部署 MobileNetV3 轻量化
平衡方案 EfficientNet-B4 性能/效率平衡

2. 关键技术点

  1. 窗口注意力：局部特征建模，降低计算量
  2. ECA模块：极低参数量，显著提升性能
  3. 多尺度特征：不同粒度行为检测
  4. 时序平滑：减少抖动，提高稳定性

3. Euro NCAP合规

  • ✅ 分心行为检测（手机使用）
  • ✅ 实时性满足要求（<50ms）
  • ✅ 准确率高于阈值（>95%）
  • ✅ 可部署到主流芯片

参考资料：

  1. Liu, Z., et al. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. ICCV 2021.
  2. Wang, Q., et al. (2020). ECA-Net: Efficient Channel Attention for Deep CNN. CVPR 2020.
  3. Euro NCAP Safe Driving Protocol 2026.
IMS研究
#DMS #深度学习 #分心检测 #Euro NCAP
Transformer在DMS中的应用：Swin Transformer驾驶员行为检测
https://dapalm.com/2026/06/16/2026-06-16-Swin-Transformer-Driver-Behavior-Detection-Implementation/
作者
Mars
发布于
2026年6月16日
许可协议