在工业环境中使用MEMS-LiDAR和混合数据实现符合GDPR的人体识别
GDPR-Compliant Person Recognition in Industrial Environments Using MEMS-LiDAR and Hybrid Data
AI总结：
本文提出利用MEMS-LiDAR和混合数据在工业环境中实现符合GDPR的人体识别，通过合成数据增强提升检测性能并减少标注工作量。
摘要：
在安全关键的工业室内空间中可靠地检测未经授权的个体至关重要，以避免工厂停机、财产损失和个人危险。传统的基于视觉的方法使用深度学习进行人体识别，虽然能提供图像信息，但对光照和可见性条件敏感，并且常常违反隐私法规，例如欧盟的通用数据保护条例（GDPR）。通常，基于深度学习的检测系统需要标注数据进行训练。然而，收集和标注此类数据过程耗时且由于手动处理可能不准确。因此，本文提出了一种基于微机电系统LiDAR（MEMS-LiDAR）的隐私合规方法，该方法仅捕获匿名的3D点云，避免个人身份特征。为了弥补记录真实LiDAR数据所需大量时间以及后续处理和标注，真实记录被增强合成场景，这些场景来自CARLA模拟框架。结果表明，混合数据使平均精度比仅使用真实数据训练的模型提高了44个百分点，同时将手动标注工作量减少了50%。因此，所提出的方法提供了一种可扩展、成本效益高的替代方案，替代纯真实数据方法，并系统地展示了合成LiDAR数据如何在工业环境中结合高性能的人体检测与GDPR合规性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
