The reliable detection of unauthorized individuals in safety-critical industrial indoor spaces is crucial to avoid plant shutdowns, property damage, and personal hazards. Conventional vision-based methods that use deep-learning approaches for person recognition provide image information but are sensitive to lighting and visibility conditions and often violate privacy regulations, such as the General Data Protection Regulation (GDPR) in the European Union. Typically, detection systems based on deep learning require annotated data for training. Collecting and annotating such data, however, is highly time-consuming and due to manual treatments not necessarily error free. Therefore, this paper presents a privacy-compliant approach based on Micro-Electro-Mechanical Systems LiDAR (MEMS-LiDAR), which exclusively captures anonymized 3D point clouds and avoids personal identification features. To compensate for the large amount of time required to record real LiDAR data and for post-processing and annotation, real recordings are augmented with synthetically generated scenes from the CARLA simulation framework. The results demonstrate that the hybrid data improves the average precision by 44 percentage points compared to a model trained exclusively with real data while reducing the manual annotation effort by 50 %. Thus, the proposed approach provides a scalable, cost-efficient alternative to purely real-data-based methods and systematically shows how synthetic LiDAR data can combine high performance in person detection with GDPR compliance in an industrial environment.

翻译：在安全关键型工业室内空间中可靠检测未授权人员，对于避免工厂停工、财产损失及人身危害至关重要。传统基于视觉的人员识别方法采用深度学习技术提供图像信息，但对光照与可见度条件敏感，且常违反隐私法规（如欧盟《通用数据保护条例》（GDPR））。基于深度学习的检测系统通常需要标注数据进行训练，然而此类数据的采集与标注耗时极长，且人工处理难以完全避免错误。为此，本文提出一种基于微机电系统激光雷达（MEMS-LiDAR）的隐私合规方案，该方案仅采集匿名化三维点云数据，避免获取个人身份特征。为弥补真实LiDAR数据采集、后处理及标注所需的大量时间，本研究通过CARLA仿真框架生成的合成场景对真实采集数据进行增强。实验结果表明：与仅使用真实数据训练的模型相比，混合数据将平均精度提升44个百分点，同时将人工标注工作量减少50%。因此，所提方案为纯真实数据方法提供了可扩展、高性价比的替代方案，并系统性地展示了合成LiDAR数据如何在工业环境中将人员检测的高性能与GDPR合规要求相结合。