RES-SE-CNN驾驶员分心检测：2025最新论文解读与代码复现

论文信息

• 标题： An intelligent network framework for driver distraction monitoring based on RES-SE-CNN
• 期刊： Scientific Reports (Nature子刊)
• 发表时间： 2025年2月26日
• DOI： 10.1038/s41598-025-91293-5
• 开源状态： 数据集公开

核心创新

该论文提出RES-SE-CNN架构，将Squeeze-and-Excitation (SE)注意力机制集成到ResNet残差块中，实现驾驶员分心状态的精准检测。核心创新点：

1. SE注意力增强：自适应学习通道权重，突出关键特征
2. 残差连接优化：解决深层网络退化问题
3. 多尺度特征融合：捕捉不同粒度的分心行为特征

方法详解

1. 网络架构

输入图像 (224×224×3)
    ↓
Conv 7×7, 64, stride 2
    ↓
MaxPool 3×3, stride 2
    ↓
┌─────────────────────────────────┐
│  Stage 1: 3 × RES-SE Block      │ → 64 channels
│  Stage 2: 4 × RES-SE Block      │ → 128 channels
│  Stage 3: 6 × RES-SE Block      │ → 256 channels
│  Stage 4: 3 × RES-SE Block      │ → 512 channels
└─────────────────────────────────┘
    ↓
Global Average Pooling
    ↓
FC 512 → num_classes
    ↓
Softmax

2. RES-SE Block详解

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

class SEBlock(nn.Module):
    """
    Squeeze-and-Excitation Block
    
    论文Section 3.2: 通过全局池化压缩空间维度，
    然后学习通道注意力权重
    """
    def __init__(self, channels: int, reduction: int = 16):
        super(SEBlock, self).__init__()
        self.squeeze = nn.AdaptiveAvgPool2d(1)
        self.excitation = nn.Sequential(
            nn.Linear(channels, channels // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(channels // reduction, channels, bias=False),
            nn.Sigmoid()
        )
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: 输入特征图, shape=(B, C, H, W)
        Returns:
            加权后的特征图
        """
        B, C, _, _ = x.size()
        
        # Squeeze: 全局平均池化
        y = self.squeeze(x).view(B, C)
        
        # Excitation: 学习通道权重
        y = self.excitation(y).view(B, C, 1, 1)
        
        # Scale: 重新加权
        return x * y.expand_as(x)


class RESSEBlock(nn.Module):
    """
    RES-SE Block: 残差块 + SE注意力
    
    论文核心模块，Figure 3架构
    """
    def __init__(self, in_channels: int, out_channels: int, stride: int = 1):
        super(RESSEBlock, self).__init__()
        
        self.conv1 = nn.Conv2d(in_channels, out_channels, 
                               kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        
        self.conv2 = nn.Conv2d(out_channels, out_channels,
                               kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        
        # SE模块
        self.se = SEBlock(out_channels, reduction=16)
        
        # 残差连接
        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 
                         kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        前向传播
        
        Args:
            x: 输入tensor, shape=(B, C_in, H, W)
        Returns:
            输出tensor, shape=(B, C_out, H', W')
        """
        identity = self.shortcut(x)
        
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        
        # 应用SE注意力
        out = self.se(out)
        
        # 残差连接
        out += identity
        out = F.relu(out)
        
        return out

3. 完整网络实现

class RESSENet(nn.Module):
    """
    RES-SE-CNN 完整网络
    
    论文Table 1配置:
    - RES-SE-18: [2, 2, 2, 2]
    - RES-SE-34: [3, 4, 6, 3]
    - RES-SE-50: [3, 4, 6, 3] (使用Bottleneck)
    """
    def __init__(self, block: nn.Module, layers: list, num_classes: int = 10):
        super(RESSENet, self).__init__()
        
        self.in_channels = 64
        
        # 初始卷积层
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        # RES-SE块
        self.layer1 = self._make_layer(block, 64, layers[0], stride=1)
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        
        # 分类头
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)
        
        # 权重初始化
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
    
    def _make_layer(self, block: nn.Module, out_channels: int, 
                    blocks: int, stride: int = 1) -> nn.Sequential:
        """构建RES-SE层"""
        layers = []
        layers.append(block(self.in_channels, out_channels, stride))
        self.in_channels = out_channels
        
        for _ in range(1, blocks):
            layers.append(block(out_channels, out_channels))
        
        return nn.Sequential(*layers)
    
    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)
        """
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.maxpool(x)
        
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        
        return x


def resse18(num_classes: int = 10) -> RESSENet:
    """RES-SE-18模型"""
    return RESSENet(RESSEBlock, [2, 2, 2, 2], num_classes)


def resse34(num_classes: int = 10) -> RESSENet:
    """RES-SE-34模型 (论文主要架构)"""
    return RESSENet(RESSEBlock, [3, 4, 6, 3], num_classes)

数据集与训练

分心类别定义

论文使用State Farm驾驶员分心数据集，定义10类分心行为：

类别ID	行为描述	IMS应用场景
C0	正常驾驶	基线参考
C1	右手发短信	视觉分心检测
C2	右手打电话	视觉分心检测
C3	左手发短信	视觉分心检测
C4	左手打电话	视觉分心检测
C5	调整收音机	操作分心检测
C6	喝水/饮料	操作分心检测
C7	伸手拿后座物品	操作分心检测
C8	整理头发/化妆	视觉分心检测
C9	与乘客交谈	认知分心参考

训练代码

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

def train_resse():
    """
    论文Section 4.1训练配置复现
    """
    # 数据预处理
    train_transform = transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.RandomCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ColorJitter(brightness=0.2, contrast=0.2),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                           std=[0.229, 0.224, 0.225])
    ])
    
    val_transform = transforms.Compose([
        transforms.Resize((224, 224)),
        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)
    val_dataset = datasets.ImageFolder('data/val', transform=val_transform)
    
    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
    val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=4)
    
    # 模型
    model = resse34(num_classes=10)
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = model.to(device)
    
    # 损失函数与优化器
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-4)
    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
    
    # 训练循环
    best_acc = 0.0
    
    for epoch in range(50):
        model.train()
        running_loss = 0.0
        
        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()
            
            running_loss += loss.item()
        
        # 验证
        model.eval()
        correct = 0
        total = 0
        
        with torch.no_grad():
            for images, labels in val_loader:
                images, labels = images.to(device), labels.to(device)
                outputs = model(images)
                _, predicted = outputs.max(1)
                total += labels.size(0)
                correct += predicted.eq(labels).sum().item()
        
        val_acc = 100.0 * correct / total
        scheduler.step()
        
        print(f"Epoch {epoch+1}/50 | Loss: {running_loss/len(train_loader):.4f} | "
              f"Val Acc: {val_acc:.2f}%")
        
        if val_acc > best_acc:
            best_acc = val_acc
            torch.save(model.state_dict(), 'resse34_best.pth')
    
    print(f"\n最佳验证准确率: {best_acc:.2f}%")
    return model


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

实验结果

性能对比

论文Table 2对比结果：

模型	参数量	FLOPs	准确率
VGG19	143.7M	19.7B	94.2%
DenseNet121	7.98M	2.87B	95.1%
ResNet50	25.6M	4.12B	95.8%
RES-SE-CNN (本文)	26.2M	4.35B	97.3%

消融实验

论文Table 3消融研究：

组件	准确率	说明
Baseline ResNet	95.8%	无SE模块
+ SE Block	96.9%	验证SE有效性
+ 数据增强	97.1%	ColorJitter + RandomCrop
+ Label Smoothing	97.3%	最终配置

IMS应用启示

1. 部署架构建议

graph LR
    A[IR摄像头] --> B[图像预处理]
    B --> C[RES-SE-CNN推理]
    C --> D[分心类别判断]
    D --> E{置信度 > 阈值?}
    E -->|是| F[触发警告]
    E -->|否| G[继续监控]
    F --> H[记录日志]

2. 嵌入式部署优化

Qualcomm QCS8255部署配置：

import onnxruntime as ort
import numpy as np

class DMSEDeployer:
    """
    RES-SE-CNN在Qualcomm NPU上的部署
    """
    def __init__(self, onnx_model_path: str):
        # QNN Execution Provider for NPU
        self.session = ort.InferenceSession(
            onnx_model_path,
            providers=['QNNExecutionProvider', 'CPUExecutionProvider']
        )
        
        # 输入输出信息
        self.input_name = self.session.get_inputs()[0].name
        self.output_name = self.session.get_outputs()[0].name
        
        # 性能统计
        self.latency_history = []
    
    def preprocess(self, image: np.ndarray) -> np.ndarray:
        """
        图像预处理
        
        Args:
            image: BGR图像, shape=(H, W, 3)
        Returns:
            归一化tensor, shape=(1, 3, 224, 224)
        """
        # BGR -> RGB
        image = image[:, :, ::-1]
        
        # Resize
        image = cv2.resize(image, (224, 224))
        
        # Normalize
        mean = np.array([0.485, 0.456, 0.406])
        std = np.array([0.229, 0.224, 0.225])
        image = (image / 255.0 - mean) / std
        
        # HWC -> CHW
        image = image.transpose(2, 0, 1)
        
        return image.astype(np.float32)[np.newaxis, ...]
    
    def infer(self, image: np.ndarray) -> tuple:
        """
        推理
        
        Returns:
            (类别ID, 置信度, 延迟ms)
        """
        import time
        
        input_tensor = self.preprocess(image)
        
        start = time.perf_counter()
        outputs = self.session.run([self.output_name], {self.input_name: input_tensor})
        latency = (time.perf_counter() - start) * 1000
        
        self.latency_history.append(latency)
        
        probs = outputs[0][0]
        class_id = np.argmax(probs)
        confidence = probs[class_id]
        
        return int(class_id), float(confidence), latency
    
    def get_performance(self) -> dict:
        """获取性能统计"""
        if not self.latency_history:
            return {}
        
        return {
            'avg_latency_ms': np.mean(self.latency_history),
            'p99_latency_ms': np.percentile(self.latency_history, 99),
            'fps': 1000 / np.mean(self.latency_history)
        }


# 使用示例
if __name__ == "__main__":
    deployer = DMSEDeployer('resse34_qnn.onnx')
    
    # 模拟测试
    import cv2
    test_image = cv2.imread('driver_test.jpg')
    
    class_id, confidence, latency = deployer.infer(test_image)
    
    distraction_labels = [
        'Normal', 'Text_Right', 'Phone_Right', 'Text_Left',
        'Phone_Left', 'Radio', 'Drinking', 'Reaching', 
        'Hair_Makeup', 'Talking'
    ]
    
    print(f"检测结果: {distraction_labels[class_id]}")
    print(f"置信度: {confidence:.2%}")
    print(f"延迟: {latency:.2f}ms")
    print(f"平均FPS: {deployer.get_performance()['fps']:.1f}")

3. 实时检测场景配置

Euro NCAP 2026分心检测场景映射：

Euro NCAP场景	RES-SE类别	检测要求	警告等级
D-01 手机使用(打电话)	C2, C4	≤3秒检测	一级
D-02 手机使用(发短信)	C1, C3	≤3秒检测	一级
D-03 手机使用(手持)	C1-C4	≤3秒检测	二级
D-04 操作中控	C5	≤3秒检测	二级
D-05 视线偏离	需结合眼动	≤3秒检测	二级

4. 硬件配置要求

典型部署方案：

组件	型号	参数	功耗
处理器	Qualcomm QCS8255	8核Kryo, Hexagon NPU 26TOPS	5-8W
IR摄像头	OV2311	2MP, 1600×1200, 全局快门	0.5W
IR补光	SFH 4740	940nm, 120mW/sr	1W
内存	LPDDR5	8GB	-

性能指标：

• 模型大小：104MB (FP32) / 26MB (INT8量化)
• 推理延迟：15-25ms (NPU)
• 帧率：40-60 FPS
• 功耗：<10W

开发落地指导

1. 数据采集规范

class DistractionDataCollector:
    """
    分心数据采集工具
    
    建议采集量:
    - 每类 ≥5000张
    - 多光照条件(白天/夜间/逆光)
    - 多佩戴物(眼镜/墨镜/口罩)
    - 多人种/年龄/性别
    """
    
    CATEGORIES = {
        'normal': 'C0',
        'text_right': 'C1',
        'phone_right': 'C2',
        'text_left': 'C3',
        'phone_left': 'C4',
        'radio': 'C5',
        'drinking': 'C6',
        'reaching': 'C7',
        'hair_makeup': 'C8',
        'talking': 'C9'
    }
    
    def __init__(self, output_dir: str):
        self.output_dir = output_dir
        self.camera = None  # 初始化摄像头
        
    def capture_sample(self, category: str, subject_id: int):
        """采集单张样本"""
        pass

2. 增量训练流程

def incremental_train(base_model_path: str, new_data_dir: str):
    """
    增量训练流程
    
    适用于:
    - 新驾驶员适配
    - 新场景迁移
    - 季节变化适应
    """
    # 加载预训练模型
    model = resse34(num_classes=10)
    model.load_state_dict(torch.load(base_model_path))
    
    # 冻结前3层，只微调最后1层
    for param in model.layer1.parameters():
        param.requires_grad = False
    for param in model.layer2.parameters():
        param.requires_grad = False
    for param in model.layer3.parameters():
        param.requires_grad = False
    
    # 使用较小学习率
    optimizer = optim.Adam(model.parameters(), lr=1e-5)
    
    # ... 训练代码

3. 模型量化部署

# Qualcomm AI Hub量化流程
pip install qai_hub_models

# 导出ONNX
python -c "from resse_model import resse34; \
           import torch; \
           model = resse34(10); \
           torch.onnx.export(model, torch.randn(1,3,224,224), 'resse34.onnx')"

# 量化为INT8
qai_hub_models.export_model(
    model='resse34.onnx',
    device='QCS8255',
    quantize=True,
    output_path='resse34_int8.bin'
)

参考文献

1. 论文原文： Shi, K., et al. (2025). An intelligent network framework for driver distraction monitoring based on RES-SE-CNN. Scientific Reports, 15, Article 91293.
2. SE-Net原始论文： Hu, J., et al. (2018). Squeeze-and-Excitation Networks. CVPR 2018.
3. Euro NCAP 2026 Protocol： Safe Driving Occupant Monitoring Protocol v1.1, October 2025.

---

总结： RES-SE-CNN通过集成SE注意力机制到ResNet架构，在驾驶员分心检测任务上达到97.3%准确率。对于IMS开发，建议：

1. 使用RES-SE-34作为基础模型
2. INT8量化后部署到Qualcomm NPU，延迟<25ms
3. 结合Euro NCAP场景定义警告策略
4. 定期增量训练适应新场景