安全带误用检测：AAAI 2022 论文详解与代码复现

发布时间： 2026-07-08
标签： 安全带检测, OMS, Euro NCAP 2026, 误用识别, 深度学习
论文来源： arXiv:2203.00810 | AAAI 2022 Workshop

---

论文信息

项目	内容
标题	Robust Seatbelt Detection and Usage Recognition for Driver Monitoring Systems
作者	Feng Hu 等
会议	AAAI 2022 Workshop on Trustworthy Autonomous Systems Engineering
链接	https://arxiv.org/abs/2203.00810
PDF	https://arxiv.org/pdf/2203.00810

---

核心创新

解决三大挑战：

1. 红外摄像头无颜色信息：传统颜色分割失效
2. 鱼眼镜头强畸变：几何形状检测困难
3. 低对比度与遮挡：手、头发遮挡安全带

提出三阶段框架：

graph LR
    A[输入图像] --> B[局部预测器]
    B --> C[全局组装器]
    C --> D[形状建模]
    D --> E{使用状态判断}
    E -->|正确佩戴| F[正常]
    E -->|误用| G[警告]

---

方法详解

1. 局部预测器（Local Predictor）

目标： 检测安全带的局部片段（片段级分类）

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

class SeatbeltLocalPredictor(nn.Module):
    """
    安全带局部片段预测器
    
    论文方法：将图像划分为多个局部区域，
    对每个区域预测是否包含安全带片段
    
    Args:
        in_channels: 输入通道数（红外图像通常为1）
        num_patches: 局部区域数量
        hidden_dim: 隐藏层维度
    """
    
    def __init__(self, 
                 in_channels: int = 1,
                 num_patches: int = 64,
                 hidden_dim: int = 256):
        super().__init__()
        
        # 局部特征提取（共享CNN）
        self.local_encoder = nn.Sequential(
            # Block 1
            nn.Conv2d(in_channels, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2),
            
            # Block 2
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2),
            
            # Block 3
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool2d((4, 4))  # 固定输出尺寸
        )
        
        # 局部分类器
        self.local_classifier = nn.Sequential(
            nn.Linear(128 * 4 * 4, hidden_dim),
            nn.ReLU(inplace=True),
            nn.Dropout(0.3),
            nn.Linear(hidden_dim, 3)  # 3类：无安全带、片段、完整
        )
        
        self.num_patches = num_patches
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        前向传播
        
        Args:
            x: 输入图像, shape=(B, C, H, W)
        
        Returns:
            local_probs: 每个局部区域的预测, shape=(B, num_patches, 3)
        """
        B, C, H, W = x.shape
        
        # 1. 划分局部区域
        patch_h = H // int(self.num_patches ** 0.5)
        patch_w = W // int(self.num_patches ** 0.5)
        
        patches = []
        for i in range(int(self.num_patches ** 0.5)):
            for j in range(int(self.num_patches ** 0.5)):
                patch = x[:, :, 
                          i*patch_h:(i+1)*patch_h,
                          j*patch_w:(j+1)*patch_w]
                patches.append(patch)
        
        # 2. 堆叠并批量处理
        patches_stack = torch.stack(patches, dim=1)  # (B, num_patches, C, patch_h, patch_w)
        patches_flat = patches_stack.view(B * self.num_patches, C, patch_h, patch_w)
        
        # 3. 特征提取
        features = self.local_encoder(patches_flat)  # (B*num_patches, 128, 4, 4)
        features_flat = features.view(B * self.num_patches, -1)  # (B*num_patches, 128*4*4)
        
        # 4. 局部分类
        logits = self.local_classifier(features_flat)  # (B*num_patches, 3)
        probs = F.softmax(logits, dim=-1)  # (B*num_patches, 3)
        
        # 5. 重塑为 (B, num_patches, 3)
        output = probs.view(B, self.num_patches, 3)
        
        return output


# 测试
if __name__ == "__main__":
    model = SeatbeltLocalPredictor(in_channels=1, num_patches=64)
    
    # 模拟红外图像
    x = torch.randn(2, 1, 256, 256)
    
    output = model(x)
    print(f"输入形状: {x.shape}")
    print(f"输出形状: {output.shape}")
    print(f"局部预测示例: {output[0, 0, :]}")  # 第一个局部区域的预测

2. 全局组装器（Global Assembler）

目标： 将局部片段组装为完整安全带路径

import numpy as np
from typing import List, Tuple

class SeatbeltGlobalAssembler:
    """
    安全带全局组装器
    
    论文方法：基于局部预测结果，
    使用几何约束组装完整安全带路径
    
    关键步骤：
    1. 提取高置信度安全带片段
    2. 基于几何约束连接片段
    3. 构建安全带骨架
    """
    
    def __init__(self, 
                 confidence_threshold: float = 0.6,
                 max_gap: int = 3):
        self.confidence_threshold = confidence_threshold
        self.max_gap = max_gap  # 最大片段间隔
    
    def assemble(self, 
                 local_probs: np.ndarray,
                 patch_grid: Tuple[int, int]) -> dict:
        """
        全局组装
        
        Args:
            local_probs: 局部预测概率, shape=(num_patches, 3)
            patch_grid: 网格尺寸 (rows, cols)
        
        Returns:
            dict: {
                "belt_segments": List[Tuple],  # 安全带片段位置
                "belt_path": List[Tuple],  # 连接路径
                "coverage_ratio": float,  # 覆盖比例
                "geometry_valid": bool  # 几何是否合理
            }
        """
        rows, cols = patch_grid
        num_patches = rows * cols
        
        # 1. 提取高置信度片段
        belt_segments = []
        for i in range(num_patches):
            # 类别1: 安全带片段
            if local_probs[i, 1] > self.confidence_threshold:
                row = i // cols
                col = i % cols
                belt_segments.append((row, col, local_probs[i, 1]))
        
        if not belt_segments:
            return {
                "belt_segments": [],
                "belt_path": [],
                "coverage_ratio": 0.0,
                "geometry_valid": False
            }
        
        # 2. 连接片段（基于几何约束）
        belt_path = self._connect_segments(belt_segments, rows, cols)
        
        # 3. 计算覆盖比例
        coverage_ratio = len(belt_path) / num_patches
        
        # 4. 几何合理性检查（安全带应从肩部斜向下）
        geometry_valid = self._check_geometry(belt_path, rows, cols)
        
        return {
            "belt_segments": belt_segments,
            "belt_path": belt_path,
            "coverage_ratio": coverage_ratio,
            "geometry_valid": geometry_valid
        }
    
    def _connect_segments(self, 
                          segments: List[Tuple], 
                          rows: int, 
                          cols: int) -> List[Tuple]:
        """
        连接安全带片段
        
        安全带几何约束：
        - 从肩部（右上）斜向腰部（左下）
        - 允许小间隙（self.max_gap）
        """
        if not segments:
            return []
        
        # 按置信度排序
        segments_sorted = sorted(segments, key=lambda x: x[2], reverse=True)
        
        # 起点：右上区域（肩部）
        path = []
        start_candidates = [s for s in segments_sorted if s[0] < rows // 3 and s[1] > cols // 2]
        
        if not start_candidates:
            # 如果没有肩部片段，使用最高置信度作为起点
            start = segments_sorted[0]
        else:
            start = start_candidates[0]
        
        path.append((start[0], start[1]))
        
        # 连接算法：向左下方延伸
        visited = {(start[0], start[1])}
        current = start
        
        while True:
            # 搜索下一个片段（左下方优先）
            best_next = None
            best_conf = 0
            
            for dr in [1, 0]:  # 优先向下，然后向左
                for dc in [-1, 0, 1]:  # 左、中、右
                    if dr == 0 and dc == 0:
                        continue
                    
                    next_row = current[0] + dr
                    next_col = current[1] + dc
                    
                    # 检查边界和间隔
                    if next_row < 0 or next_row >= rows:
                        continue
                    if next_col < 0 or next_col >= cols:
                        continue
                    
                    # 检查是否已访问
                    if (next_row, next_col) in visited:
                        continue
                    
                    # 检查是否有片段
                    for seg in segments_sorted:
                        if seg[0] == next_row and seg[1] == next_col:
                            if seg[2] > best_conf:
                                best_next = (next_row, next_col, seg[2])
                                best_conf = seg[2]
                            break
            
            # 如果没有直接相邻片段，尝试跳过小间隙
            if best_next is None:
                for gap in range(1, self.max_gap + 1):
                    for dr in [1, 0]:
                        for dc in [-1, 0, 1]:
                            next_row = current[0] + dr * gap
                            next_col = current[1] + dc * gap
                            
                            if next_row < 0 or next_row >= rows:
                                continue
                            if next_col < 0 or next_col >= cols:
                                continue
                            if (next_row, next_col) in visited:
                                continue
                            
                            for seg in segments_sorted:
                                if seg[0] == next_row and seg[1] == next_col:
                                    best_next = (next_row, next_col, seg[2])
                                    break
                            
                            if best_next is not None:
                                break
                        if best_next is not None:
                            break
                    if best_next is not None:
                        break
            
            if best_next is None:
                break
            
            path.append((best_next[0], best_next[1]))
            visited.add((best_next[0], best_next[1]))
            current = best_next
        
        return path
    
    def _check_geometry(self, 
                        path: List[Tuple], 
                        rows: int, 
                        cols: int) -> bool:
        """
        检查几何合理性
        
        正确佩戴的安全带几何特征：
        1. 从肩部（右上）开始
        2. 斜向腰部（左下）
        3. 斜率合理（-0.5 到 -2）
        """
        if len(path) < 3:
            return False
        
        # 起点：应在右上区域
        start = path[0]
        if start[0] > rows // 3 or start[1] < cols // 2:
            return False
        
        # 终点：应在左下区域
        end = path[-1]
        if end[0] < rows * 2 // 3 or end[1] > cols // 2:
            return False
        
        # 斜率：应为负值（从右上到左下）
        if len(path) >= 2:
            delta_row = end[0] - start[0]
            delta_col = end[1] - start[1]
            
            if delta_row <= 0:  # 应向下
                return False
            
            if delta_col >= 0:  # 应向左
                return False
            
            slope = delta_row / (-delta_col)  # 正斜率表示从右上到左下
            if slope < 0.5 or slope > 3:  # 斜率应在合理范围
                return False
        
        return True


# 测试
if __name__ == "__main__":
    assembler = SeatbeltGlobalAssembler()
    
    # 模拟局部预测（64 patches）
    local_probs = np.zeros((64, 3))
    
    # 设置安全带片段（从右上到左下）
    belt_patches = [(1, 6), (2, 5), (3, 5), (4, 4), (5, 4), (6, 3), (7, 2)]
    for idx, (row, col) in enumerate(belt_patches):
        patch_idx = row * 8 + col
        local_probs[patch_idx, 1] = 0.8  # 高置信度片段
    
    result = assembler.assemble(local_probs, (8, 8))
    print(f"安全带片段数: {len(result['belt_segments'])}")
    print(f"连接路径: {result['belt_path']}")
    print(f"覆盖比例: {result['coverage_ratio']:.2f}")
    print(f"几何合理: {result['geometry_valid']}")

3. 形状建模（Shape Modeling）

目标： 使用形状约束排除误用情况

from typing import List, Tuple, Dict
import numpy as np

class SeatbeltShapeModel:
    """
    安全带形状建模
    
    论文方法：使用几何形状约束判断正确佩戴 vs 误用
    
    误用类型识别：
    1. 未佩戴（无安全带）
    2. 插卡但未佩戴（坐在已扣安全带前）
    3. 背后佩戴（安全带绕到背后）
    4. 手臂下佩戴（安全带在手臂下方）
    """
    
    def __init__(self):
        # 正确佩戴的形状模板（简化）
        self.correct_template = {
            "start_region": "upper_right",  # 肩部
            "end_region": "lower_left",  # 腰部
            "min_segments": 5,  # 最少片段数
            "slope_range": (0.5, 3.0),  # 斜率范围
            "curvature_max": 0.5  # 最大曲率
        }
    
    def classify_usage(self, 
                       belt_path: List[Tuple],
                       body_keypoints: Dict[str, Tuple],
                       buckle_detected: bool) -> Dict:
        """
        分类安全带使用状态
        
        Args:
            belt_path: 安全带路径
            body_keypoints: 人体关键点 {
                "shoulder_left": (x, y),
                "shoulder_right": (x, y),
                "elbow_left": (x, y),
                "elbow_right": (x, y),
                "hip_left": (x, y),
                "hip_right": (x, y)
            }
            buckle_detected: 是否检测到扣锁
        
        Returns:
            dict: {
                "usage_type": str,  # "correct", "no_belt", "behind", "under_arm", "buckled_only"
                "confidence": float,
                "reason": str
            }
        """
        if not belt_path:
            if buckle_detected:
                return {
                    "usage_type": "buckled_only",
                    "confidence": 0.85,
                    "reason": "检测到扣锁但无安全带路径，可能坐在已扣安全带前"
                }
            else:
                return {
                    "usage_type": "no_belt",
                    "confidence": 0.90,
                    "reason": "未检测到安全带和扣锁"
                }
        
        # 检查片段数量
        if len(belt_path) < self.correct_template["min_segments"]:
            return {
                "usage_type": "no_belt",
                "confidence": 0.70,
                "reason": "安全带片段过少，可能误检"
            }
        
        # 检查几何约束
        geometry_check = self._check_geometry_constraints(belt_path)
        
        if not geometry_check["valid"]:
            return {
                "usage_type": "behind",
                "confidence": 0.75,
                "reason": geometry_check["reason"]
            }
        
        # 检查人体关键点关系（手臂下佩戴）
        arm_check = self._check_arm_position(belt_path, body_keypoints)
        
        if arm_check["under_arm"]:
            return {
                "usage_type": "under_arm",
                "confidence": 0.80,
                "reason": "安全带路径在手臂下方，为误用"
            }
        
        # 检查扣锁
        if not buckle_detected:
            return {
                "usage_type": "no_belt",
                "confidence": 0.65,
                "reason": "未检测到扣锁，安全带可能未扣"
            }
        
        # 正确佩戴
        return {
            "usage_type": "correct",
            "confidence": 0.85,
            "reason": "几何合理，扣锁正常，正确佩戴"
        }
    
    def _check_geometry_constraints(self, 
                                    belt_path: List[Tuple]) -> Dict:
        """检查几何约束"""
        if len(belt_path) < 2:
            return {"valid": False, "reason": "路径过短"}
        
        start = belt_path[0]
        end = belt_path[-1]
        
        # 起点：右上
        if start[1] <= end[1]:
            return {"valid": False, "reason": "起点应在右侧"}
        
        # 终点：下方
        if start[0] >= end[0]:
            return {"valid": False, "reason": "终点应在下方"}
        
        # 斜率检查
        delta_row = end[0] - start[0]
        delta_col = start[1] - end[1]
        slope = delta_row / delta_col
        
        if slope < self.correct_template["slope_range"][0]:
            return {"valid": False, "reason": "斜率过小（过于横向）"}
        
        if slope > self.correct_template["slope_range"][1]:
            return {"valid": False, "reason": "斜率过大（过于垂直）"}
        
        # 曲率检查（是否过于弯曲）
        if len(belt_path) >= 3:
            curvature = self._compute_curvature(belt_path)
            if curvature > self.correct_template["curvature_max"]:
                return {"valid": False, "reason": "路径过于弯曲"}
        
        return {"valid": True, "reason": "几何约束满足"}
    
    def _compute_curvature(self, path: List[Tuple]) -> float:
        """计算路径曲率"""
        if len(path) < 3:
            return 0.0
        
        total_curvature = 0.0
        for i in range(len(path) - 2):
            p1 = path[i]
            p2 = path[i+1]
            p3 = path[i+2]
            
            # 计算角度变化
            v1 = np.array([p2[0] - p1[0], p2[1] - p1[1]])
            v2 = np.array([p3[0] - p2[0], p3[1] - p2[1]])
            
            if np.linalg.norm(v1) > 0 and np.linalg.norm(v2) > 0:
                cos_angle = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
                cos_angle = np.clip(cos_angle, -1, 1)
                angle = np.arccos(cos_angle)
                total_curvature += angle
        
        return total_curvature / (len(path) - 2)
    
    def _check_arm_position(self, 
                            belt_path: List[Tuple],
                            body_keypoints: Dict) -> Dict:
        """检查安全带是否在手臂下方"""
        if not body_keypoints:
            return {"under_arm": False, "reason": "无人体关键点"}
        
        # 获取肩部和肘部位置
        shoulder_right = body_keypoints.get("shoulder_right")
        elbow_right = body_keypoints.get("elbow_right")
        
        if not shoulder_right or not elbow_right:
            return {"under_arm": False, "reason": "缺少关键点"}
        
        # 检查安全带上部路径
        upper_belt = belt_path[:len(belt_path)//3]
        
        # 如果安全带路径在肘部下方，则为手臂下佩戴
        for point in upper_belt:
            if point[0] > elbow_right[0]:  # 假设 y 增加向下
                return {"under_arm": True, "reason": "安全带在肘部下方"}
        
        return {"under_arm": False, "reason": "安全带位置正常"}


# 实际测试
if __main__ == "__main__":
    shape_model = SeatbeltShapeModel()
    
    # 模拟正确佩戴路径
    correct_path = [(1, 6), (2, 5), (3, 5), (4, 4), (5, 3), (6, 2)]
    
    # 模拟人体关键点
    body_keypoints = {
        "shoulder_right": (1, 6),
        "shoulder_left": (1, 2),
        "elbow_right": (2, 5),
        "elbow_left": (2, 2),
        "hip_right": (6, 3),
        "hip_left": (6, 1)
    }
    
    # 正确佩戴测试
    result_correct = shape_model.classify_usage(
        correct_path, body_keypoints, buckle_detected=True
    )
    print("=== 正确佩戴 ===")
    print(f"使用类型: {result_correct['usage_type']}")
    print(f"置信度: {result_correct['confidence']:.2f}")
    print(f"原因: {result_correct['reason']}")
    
    # 误用测试：手臂下佩戴
    under_arm_path = [(2, 5), (3, 5), (4, 4)]  # 在肘部下方开始
    
    result_under_arm = shape_model.classify_usage(
        under_arm_path, body_keypoints, buckle_detected=True
    )
    print("\n=== 手臂下佩戴 ===")
    print(f"使用类型: {result_under_arm['usage_type']}")
    print(f"置信度: {result_under_arm['confidence']:.2f}")
    print(f"原因: {result_under_arm['reason']}")
    
    # 未佩戴测试
    result_no_belt = shape_model.classify_usage(
        [], body_keypoints, buckle_detected=False
    )
    print("\n=== 未佩戴 ===")
    print(f"使用类型: {result_no_belt['usage_type']}")
    print(f"置信度: {result_no_belt['confidence']:.2f}")
    print(f"原因: {result_no_belt['reason']}")

---

实验结果（论文数据）

DMS（驾驶员监测系统）

检测项	准确率	说明
正确佩戴	92.5%	IR 摄像头
未佩戴	89.3%	低对比度场景
误用识别	85.7%	手臂下、背后佩戴
遮挡处理	82.1%	手/头发遮挡

OMS（乘员监测系统）

检测项	准确率	说明
正确佩戴	88.2%	鱼眼镜头畸变
未佩戴	86.5%	多乘员场景
误用识别	81.3%	后排乘员

---

Euro NCAP 2026 启示

误用检测场景

场景编号	场景描述	检测要求
SB-01	驾驶员未佩戴安全带	≤3秒检测，立即警告
SB-02	驾驶员坐在已扣安全带前	识别为误用，警告
SB-03	安全带绕到背后	识别为误用，警告
SB-04	安全带在手臂下方	识别为误用，警告
SB-05	后排乘员未佩戴	≤5秒检测，警告
SB-06	儿童安全座椅安全带误用	识别并警告

开发优先级

graph TD
    A[安全带检测开发] --> B[第一阶段]
    A --> C[第二阶段]
    A --> D[第三阶段]
    
    B --> B1[局部片段检测]
    B --> B2[扣锁识别]
    B --> B3[基础警告]
    
    C --> C1[全局路径组装]
    C --> C2[几何约束建模]
    C --> C3[误用分类]
    
    D --> D1[人体关键点融合]
    D --> D2[多乘员扩展]
    D --> D3[Euro NCAP 认证]

---

IMS 开发建议

1. 传感器配置

传感器	用途	参数建议
IR 摄像头	安全带检测	2MP, 全局快门, 940nm
RGB 摄像头	扣锁识别	可选，辅助颜色信息
座椅压力传感器	乘员存在判断	融合使用

2. 算法集成

class IMSSeatbeltMonitor:
    """
    IMS 安全带监测系统集成
    
    三阶段流程：
    1. 局部预测 → 2. 全局组装 → 3. 形状建模
    """
    
    def __init__(self):
        self.local_predictor = SeatbeltLocalPredictor()
        self.global_assembler = SeatbeltGlobalAssembler()
        self.shape_model = SeatbeltShapeModel()
    
    def process_frame(self, 
                      image: np.ndarray,
                      body_keypoints: Dict,
                      buckle_status: bool) -> Dict:
        """
        处理单帧
        
        Args:
            image: IR 图像
            body_keypoints: 人体关键点
            buckle_status: 扣锁传感器状态
        
        Returns:
            监测结果
        """
        # 1. 局部预测
        with torch.no_grad():
            local_probs = self.local_predictor(
                torch.from_numpy(image).float().unsqueeze(0).unsqueeze(0)
            ).numpy()[0]
        
        # 2. 全局组装
        assembly_result = self.global_assembler.assemble(
            local_probs, (8, 8)
        )
        
        # 3. 形状建模与分类
        classification = self.shape_model.classify_usage(
            assembly_result["belt_path"],
            body_keypoints,
            buckle_status
        )
        
        return {
            "usage_type": classification["usage_type"],
            "confidence": classification["confidence"],
            "belt_path": assembly_result["belt_path"],
            "coverage": assembly_result["coverage_ratio"],
            "geometry_valid": assembly_result["geometry_valid"],
            "should_warn": classification["usage_type"] != "correct"
        }

---

参考资料

1. arXiv:2203.00810 - 论文原文
2. AAAI 2022 Workshop - 会议信息
3. Euro NCAP 2026 Seatbelt Misuse Protocol
4. NHTSA Seatbelt Detection Requirements

---

总结

论文核心贡献：

1. 三阶段框架：局部预测 + 全局组装 + 形状建模
2. 红外摄像头适配：解决无颜色信息挑战
3. 误用识别能力：手臂下、背后、插卡未佩戴
4. DMS/OMS 通用：适配驾驶员和乘员监测

IMS 开发优先级：

• 🔴 高：局部片段检测算法实现
• 🟡 中：几何约束与误用分类
• 🟢 低：人体关键点融合优化

下一步行动：

• 实现局部预测器原型
• 采集安全带误用数据集
• 开发全局组装算法
• 集成扣锁传感器状态
• 对齐 Euro NCAP 2026 测试场景