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.

翻译：工业中采用人工智能驱动的计算机视觉常受限于需在操作效用与工人隐私之间取得平衡。基于我们先前提出的隐私保护框架，本文首次对其在工业合作伙伴于实际生产环境中直接采集的真实数据上进行了全面验证。我们在三个代表性应用场景中评估该框架：木工生产监控、人感知自动导引车导航以及多摄像头工效学风险评估。该方法采用学习的视觉变换，在保留任务性能所需关键特征的同时，模糊敏感或与任务无关的信息。通过对隐私-效用权衡的量化评估及工业合作伙伴的定性反馈，我们评估了框架的有效性、部署可行性及信任影响。结果表明，任务特定的模糊处理能以降低隐私风险实现有效监控，证实了该框架具备实际应用条件，并为工业中负责任、以人为本的人工智能部署提供了跨领域建议。