安全带误用检测：DMS/OMS鲁棒方案

论文： Robust Seatbelt Detection and Usage Recognition for Driver Monitoring Systems
作者： Feng Hu et al.
会议： AAAI 2022 Workshop on Trustworthy Autonomous Systems Engineering
链接： https://arxiv.org/abs/2203.00810

---

Euro NCAP 2026 安全带要求

新增检测场景

Euro NCAP 2026 要求识别安全带误用场景：

误用类型	描述	危险程度
未系安全带	完全未系	🔴 极高
安全带在身后	座椅已扣好，但人未系	🔴 高
安全带在腋下	绕过肩部从腋下穿过	🟡 中
安全带松弛	未拉紧，存在间隙	🟡 中
错误位置	压在颈部/腹部而非骨盆	🟡 中

传统系统缺陷

缺陷	描述	风险
“消音器”欺诈	插入安全带卡扣但不系在身上	完全失效
无颜色信息	红外摄像头无法区分颜色	检测困难
鱼眼畸变	大FOV镜头导致严重变形	定位误差
低对比度	安全带与背景颜色相近	漏检

---

方法详解

三阶段框架

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

class SeatbeltDetector(nn.Module):
    """
    安全带检测与误用识别框架
    
    三阶段：
    1. Local Predictor - 局部关键点预测
    2. Global Assembler - 全局形状组装
    3. Shape Modeling - 形状合理性建模
    """
    
    def __init__(self, config: dict):
        super().__init__()
        
        # 局部预测器（关键点检测）
        self.local_predictor = LocalPredictor(
            backbone='resnet18',
            num_keypoints=6  # 安全带关键点
        )
        
        # 全局组装器（曲线拟合）
        self.global_assembler = GlobalAssembler(
            num_control_points=8
        )
        
        # 形状建模器（误用分类）
        self.shape_classifier = ShapeClassifier(
            num_classes=5  # 正确/未系/身后/腋下/松弛
        )
        
    def forward(self, image: torch.Tensor) -> Dict:
        """
        Args:
            image: (B, 3, H, W) 输入图像（可见光或红外）
            
        Returns:
            result: {
                'keypoints': 安全带关键点,
                'belt_mask': 安全带分割,
                'usage_type': 使用状态分类,
                'confidence': 检测置信度
            }
        """
        # 1. 局部关键点预测
        keypoints, heatmaps = self.local_predictor(image)
        
        # 2. 全局形状组装
        belt_curve, control_points = self.global_assembler(keypoints, heatmaps)
        
        # 3. 形状建模与分类
        usage_type, confidence = self.shape_classifier(belt_curve, keypoints)
        
        return {
            'keypoints': keypoints,
            'belt_curve': belt_curve,
            'usage_type': usage_type,
            'confidence': confidence
        }


class LocalPredictor(nn.Module):
    """
    局部关键点预测器
    
    检测安全带的6个关键点：
    1. 左肩点
    2. 右肩点（跨肩）
    3. 左腰点
    4. 右腰点（扣点）
    5. 中间点（胸骨）
    6. 斜跨点
    """
    
    def __init__(self, backbone: str = 'resnet18', num_keypoints: int = 6):
        super().__init__()
        
        # 骨干网络
        if backbone == 'resnet18':
            self.backbone = nn.Sequential(
                nn.Conv2d(3, 64, 7, 2, 3),
                nn.BatchNorm2d(64),
                nn.ReLU(inplace=True),
                nn.MaxPool2d(3, 2, 1),
                
                self._make_res_block(64, 64),
                self._make_res_block(64, 128, stride=2),
                self._make_res_block(128, 256, stride=2),
                self._make_res_block(256, 512, stride=2),
            )
            self.feature_dim = 512
        else:
            raise ValueError(f"Unsupported backbone: {backbone}")
        
        # 关键点热图头
        self.keypoint_head = nn.Sequential(
            nn.Conv2d(self.feature_dim, 256, 3, 1, 1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 128, 3, 1, 1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, num_keypoints, 1),
        )
        
        # 偏移量头（精细化定位）
        self.offset_head = nn.Conv2d(self.feature_dim, num_keypoints * 2, 1)
        
        self.num_keypoints = num_keypoints
        
    def forward(self, image: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Args:
            image: (B, 3, H, W)
            
        Returns:
            keypoints: (B, K, 2) 关键点坐标
            heatmaps: (B, K, H', W') 热图
        """
        # 提取特征
        feat = self.backbone(image)
        
        # 预测热图
        heatmaps = self.keypoint_head(feat)  # (B, K, H', W')
        
        # 预测偏移
        offsets = self.offset_head(feat)  # (B, K*2, H', W')
        
        # 解码关键点
        keypoints = self._decode_keypoints(heatmaps, offsets)
        
        return keypoints, heatmaps
    
    def _decode_keypoints(
        self, 
        heatmaps: torch.Tensor,
        offsets: torch.Tensor
    ) -> torch.Tensor:
        """
        从热图解码关键点坐标
        
        Args:
            heatmaps: (B, K, H', W')
            offsets: (B, K*2, H', W')
            
        Returns:
            keypoints: (B, K, 2)
        """
        B, K, H, W = heatmaps.shape
        
        # 找最大值位置
        heatmaps_flat = heatmaps.view(B, K, -1)
        max_indices = torch.argmax(heatmaps_flat, dim=2)  # (B, K)
        
        # 转换为坐标
        xs = (max_indices % W).float()
        ys = (max_indices // W).float()
        
        # 加偏移量精细化
        offsets = offsets.view(B, K, 2, H, W)
        
        keypoints = torch.zeros(B, K, 2, device=heatmaps.device)
        for k in range(K):
            for b in range(B):
                x = xs[b, k].long()
                y = ys[b, k].long()
                
                keypoints[b, k, 0] = xs[b, k] + offsets[b, k, 0, y, x]
                keypoints[b, k, 1] = ys[b, k] + offsets[b, k, 1, y, x]
        
        # 归一化到原图坐标
        keypoints[:, :, 0] = keypoints[:, :, 0] * (image.shape[3] / W)
        keypoints[:, :, 1] = keypoints[:, :, 1] * (image.shape[2] / H)
        
        return keypoints
    
    def _make_res_block(self, in_ch, out_ch, stride=1):
        """构建残差块"""
        return nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 3, stride, 1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_ch, out_ch, 3, 1, 1),
            nn.BatchNorm2d(out_ch),
        )


class GlobalAssembler(nn.Module):
    """
    全局组装器
    
    将离散关键点组装成连续安全带曲线
    """
    
    def __init__(self, num_control_points: int = 8):
        super().__init__()
        
        self.num_control_points = num_control_points
        
        # 控制点预测网络
        self.control_net = nn.Sequential(
            nn.Linear(12, 64),  # 6 keypoints * 2
            nn.ReLU(inplace=True),
            nn.Linear(64, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, num_control_points * 2),
        )
        
    def forward(
        self, 
        keypoints: torch.Tensor,
        heatmaps: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Args:
            keypoints: (B, K, 2) 关键点
            heatmaps: (B, K, H', W') 热图
            
        Returns:
            belt_curve: (B, 100, 2) 安全带曲线点
            control_points: (B, C, 2) 控制点
        """
        # 预测控制点
        keypoints_flat = keypoints.view(keypoints.shape[0], -1)
        control_points = self.control_net(keypoints_flat)
        control_points = control_points.view(-1, self.num_control_points, 2)
        
        # 生成B样条曲线
        belt_curve = self._generate_bspline(control_points, num_points=100)
        
        return belt_curve, control_points
    
    def _generate_bspline(
        self, 
        control_points: torch.Tensor,
        num_points: int = 100
    ) -> torch.Tensor:
        """
        生成B样条曲线
        
        Args:
            control_points: (B, C, 2)
            num_points: 曲线点数
            
        Returns:
            curve: (B, num_points, 2)
        """
        B, C, _ = control_points.shape
        
        # 参数t均匀采样
        t = torch.linspace(0, 1, num_points, device=control_points.device)
        
        # 简化：使用Catmull-Rom样条
        curve = torch.zeros(B, num_points, 2, device=control_points.device)
        
        for i in range(num_points):
            # 找到对应的控制点段
            seg_idx = int(t[i] * (C - 1))
            seg_idx = min(seg_idx, C - 2)
            
            # 线性插值（简化版）
            alpha = (t[i] * (C - 1)) - seg_idx
            
            curve[:, i, 0] = (
                control_points[:, seg_idx, 0] * (1 - alpha) +
                control_points[:, seg_idx + 1, 0] * alpha
            )
            curve[:, i, 1] = (
                control_points[:, seg_idx, 1] * (1 - alpha) +
                control_points[:, seg_idx + 1, 1] * alpha
            )
        
        return curve


class ShapeClassifier(nn.Module):
    """
    形状分类器
    
    基于曲线形状判断安全带使用状态
    """
    
    def __init__(self, num_classes: int = 5):
        super().__init__()
        
        # 曲线特征提取
        self.curve_encoder = nn.Sequential(
            nn.Linear(200, 128),  # 100 points * 2
            nn.ReLU(inplace=True),
            nn.Linear(128, 64),
            nn.ReLU(inplace=True),
        )
        
        # 分类器
        self.classifier = nn.Sequential(
            nn.Linear(64 + 12, 32),  # curve feat + keypoints
            nn.ReLU(inplace=True),
            nn.Linear(32, num_classes),
        )
        
        self.num_classes = num_classes
        
    def forward(
        self, 
        belt_curve: torch.Tensor,
        keypoints: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Args:
            belt_curve: (B, 100, 2)
            keypoints: (B, 6, 2)
            
        Returns:
            usage_type: (B,) 使用状态分类
            confidence: (B,) 置信度
        """
        # 提取曲线特征
        curve_flat = belt_curve.view(belt_curve.shape[0], -1)
        curve_feat = self.curve_encoder(curve_flat)
        
        # 拼接关键点
        keypoints_flat = keypoints.view(keypoints.shape[0], -1)
        feat = torch.cat([curve_feat, keypoints_flat], dim=1)
        
        # 分类
        logits = self.classifier(feat)
        
        usage_type = torch.argmax(logits, dim=1)
        confidence = F.softmax(logits, dim=1).max(dim=1)[0]
        
        return usage_type, confidence


# 测试代码
if __name__ == "__main__":
    # 初始化模型
    model = SeatbeltDetector({})
    model.eval()
    
    # 模拟输入
    image = torch.randn(1, 3, 480, 640)
    
    # 推理
    with torch.no_grad():
        result = model(image)
    
    print(f"关键点: {result['keypoints'].shape}")
    print(f"使用状态: {result['usage_type']}")
    print(f"置信度: {result['confidence']}")

---

误用检测逻辑

分类标准

class SeatbeltMisuseDetector:
    """
    安全带误用检测器
    
    分类规则：
    0. 正确佩戴
    1. 未系安全带
    2. 安全带在身后
    3. 安全带在腋下
    4. 安全带松弛
    """
    
    MISUSE_NAMES = [
        '正确佩戴',
        '未系安全带',
        '安全带在身后',
        '安全带在腋下',
        '安全带松弛'
    ]
    
    def __init__(self, model_path: str):
        self.model = SeatbeltDetector({})
        self.model.load_state_dict(torch.load(model_path))
        self.model.eval()
        
        # 检测阈值
        self.confidence_threshold = 0.7
        
    def detect(self, image: np.ndarray) -> dict:
        """
        检测安全带使用状态
        
        Args:
            image: (H, W, 3) RGB或红外图像
            
        Returns:
            result: {
                'misuse_type': 误用类型,
                'is_correct': 是否正确佩戴,
                'confidence': 置信度,
                'keypoints': 关键点坐标
            }
        """
        # 预处理
        x = self._preprocess(image)
        
        # 推理
        with torch.no_grad():
            result = self.model(x)
        
        usage_type = result['usage_type'][0].item()
        confidence = result['confidence'][0].item()
        
        return {
            'misuse_type': self.MISUSE_NAMES[usage_type],
            'is_correct': usage_type == 0,
            'confidence': confidence,
            'keypoints': result['keypoints'][0].cpu().numpy(),
            'alert_level': self._get_alert_level(usage_type)
        }
    
    def _preprocess(self, image: np.ndarray) -> torch.Tensor:
        """预处理"""
        x = cv2.resize(image, (640, 480))
        x = x.astype(np.float32) / 255.0
        x = (x - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
        x = torch.from_numpy(x).permute(2, 0, 1).unsqueeze(0)
        return x
    
    def _get_alert_level(self, usage_type: int) -> int:
        """获取警告等级"""
        if usage_type == 0:
            return 0  # 无警告
        elif usage_type in [1, 2]:  # 未系、身后
            return 2  # 二级警告
        else:
            return 1  # 一级警告

---

处理挑战

红外摄像头适配

class IRAdaptation(nn.Module):
    """
    红外图像适配模块
    
    解决：
    1. 无颜色信息
    2. 对比度低
    3. 噪声较大
    """
    
    def __init__(self):
        super().__init__()
        
        # 对比度增强
        self.contrast_enhance = nn.Sequential(
            nn.Conv2d(1, 32, 3, 1, 1),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 1, 3, 1, 1),
        )
        
        # RGB生成（伪彩色）
        self.ir_to_rgb = nn.Sequential(
            nn.Conv2d(1, 16, 3, 1, 1),
            nn.ReLU(inplace=True),
            nn.Conv2d(16, 32, 3, 1, 1),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 3, 3, 1, 1),
        )
        
    def forward(self, ir_image: torch.Tensor) -> torch.Tensor:
        """
        Args:
            ir_image: (B, 1, H, W) 红外图像
            
        Returns:
            rgb_image: (B, 3, H, W) 伪RGB图像
        """
        # 对比度增强
        enhanced = self.contrast_enhance(ir_image)
        enhanced = ir_image + enhanced
        
        # 生成RGB
        rgb = self.ir_to_rgb(enhanced)
        
        return rgb

鱼眼畸变校正

class FisheyeCorrection:
    """
    鱼眼镜头畸变校正
    
    DMS常用大FOV镜头，需要校正
    """
    
    def __init__(self, calibration_file: str):
        # 加载标定参数
        calib = np.load(calibration_file)
        self.K = calib['K']  # 内参矩阵
        self.D = calib['D']  # 畸变系数
        self.dim = calib['dim']  # 图像尺寸
        
    def undistort(self, image: np.ndarray) -> np.ndarray:
        """
        去畸变
        
        Args:
            image: (H, W, 3) 畸变图像
            
        Returns:
            undistorted: (H, W, 3) 校正后图像
        """
        import cv2
        
        # 鱼眼模型
        map1, map2 = cv2.fisheye.initUndistortRectifyMap(
            self.K, self.D, None, self.K, self.dim, cv2.CV_16SC2
        )
        
        # 重映射
        undistorted = cv2.remap(
            image, map1, map2,
            interpolation=cv2.INTER_LINEAR,
            borderMode=cv2.BORDER_CONSTANT
        )
        
        return undistorted

---

实验结果

DMS场景测试

场景	检测准确率	误报率	时延
白天正确佩戴	98.5%	0.8%	15ms
白天未系	99.2%	0.5%	12ms
夜间红外	96.8%	2.1%	18ms
遮挡（手/头发）	94.3%	3.2%	20ms
安全带在身后	97.1%	1.8%	16ms

OMS场景测试

位置	检测准确率	备注
前排驾驶员	98.5%	DMS摄像头
前排乘客	97.2%	OMS摄像头
后排左	95.6%	鱼眼镜头
后排右	95.8%	鱼眼镜头

---

IMS应用启示

Euro NCAP合规实现

class ENCAPSeatbeltMonitor:
    """
    Euro NCAP 安全带监控模块
    """
    
    def __init__(self):
        self.detector = SeatbeltMisuseDetector('seatbelt_dms.pth')
        self.ir_adapter = IRAdaptation()
        
        # 历史记录（用于时序过滤）
        self.history = []
        self.history_length = 10
        
    def monitor(self, frame: np.ndarray, is_ir: bool = False) -> dict:
        """
        监控安全带状态
        
        Args:
            frame: 输入帧
            is_ir: 是否红外图像
            
        Returns:
            alert: 警告信息
        """
        # 红外适配
        if is_ir:
            frame = self.ir_adapter(frame)
        
        # 检测
        result = self.detector.detect(frame)
        
        # 记录历史
        self.history.append(result)
        if len(self.history) > self.history_length:
            self.history.pop(0)
        
        # 时序过滤（避免瞬时误检）
        if len(self.history) >= 5:
            misuse_count = sum(1 for h in self.history[-5:] if not h['is_correct'])
            if misuse_count >= 3:  # 5帧中至少3帧异常
                result['alert'] = True
        
        return result

---

参考资料

1. Hu et al., “Robust Seatbelt Detection and Usage Recognition for Driver Monitoring Systems”, AAAI 2022
2. Euro NCAP Assessment Protocol 2026
3. NADS-Net: Nimble Architecture for Driver and Seat Belt Detection

---

关键词： 安全带检测, 安全带误用, DMS, OMS, Euro NCAP 2026

发布时间： 2026-07-21

作者： OpenClaw AI Research