用视觉模糊技术在保护工人隐私的同时实现工厂智能监控。
Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing
- 通过学习的视觉变换隐藏敏感信息，保留任务所需特征。
- 三类工业场景中实现有效监控且降低隐私风险。
- 适合关注人本主义工业AI部署的制造企业与研究者。
工业界采用基于AI的计算机视觉常面临操作效能与员工隐私之间的权衡。本文基于此前提出的隐私保护框架，在工业合作伙伴提供的真实生产环境数据上首次完成全面验证。评估涵盖三大典型应用场景：木工生产监控、人机协作自动导引车导航、多摄像头人体工学风险评估。该方法利用学习的视觉变换，遮蔽敏感或任务无关信息，同时保留对任务至关重要的特征。通过量化隐私-效用权衡分析及工业伙伴的定性反馈，评估框架的有效性、可部署性与信任影响。结果表明，特定任务的模糊处理可在显著降低隐私风险的同时实现有效监控，证实该框架具备实际应用条件，并为跨领域负责任的人本化工业AI部署提供实践建议。
原文摘要 · Abstract (English)
The adoption of AI-powered computer vision in industry is often constrained by the need to balance operational utility with worker privacy. Building on our previously proposed privacy-preserving framework, this paper presents its first comprehensive validation on real-world data collected directly by industrial partners in active production environments. We evaluate the framework across three representative use cases: woodworking production monitoring, human-aware AGV navigation, and multi-camera ergonomic risk assessment. The approach employs learned visual transformations that obscure sensitive or task-irrelevant information while retaining features essential for task performance. Through both quantitative evaluation of the privacy-utility trade-off and qualitative feedback from industrial partners, we assess the framework's effectiveness, deployment feasibility, and trust implications. Results demonstrate that task-specific obfuscation enables effective monitoring with reduced privacy risks, establishing the framework's readiness for real-world adoption and providing cross-domain recommendations for responsible, human-centric AI deployment in industry.
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