An Auditable Downstream Safety-Intelligence Framework for Manufacturing Workplaces: Reliability-Aware PPE Evidence, Regulation-Grounded Retrieval, and Role-Specific Decision Support

Abstract

Vision-based industrial safety monitoring increasingly integrates heterogeneous accident, personal protective equipment (PPE), and semantic evidence, yet preserving their reliability, uncertainty, and provenance throughout downstream decision support remains challenging. This study proposes an auditable downstream safety-intelligence framework that transforms frozen multimodal perception outputs into structured event evidence. Worker-centered temporal PPE observations are processed into Candidate and Confirmed operational evidence while accident-related uncertainty is preserved. In parallel, regulation-grounded retrieval maps the same event evidence to candidate regulatory context from a frozen Korean industrial-safety corpus, after which manager- and worker-oriented outputs are generated under distinct information boundaries and independently verified. On a fixed internal benchmark of 150 videos and 3,000 frames, the Full-clean configuration achieved 98.23% frame-level accuracy, 97.47% Macro-F1, and 98.67% video-level accuracy. Raw PPE-violation detection achieved an F1-score of 99.31% on the ground-truth normal-scene stratum. Controlled Legal-RAG regression reproduced 117/117 invariant retrievals, preserved query scope in 32/32 evidence-change cases, and produced the expected retrieval change in 1/1 query-changed case. All 234 notification–report links were audience- and event-consistent, while all 44 PPE semantic checks and 82 verifier findings satisfied predefined validation criteria. The framework supports provenance-preserving human review rather than autonomous safety or legal determination.

The authors' abstract, as published at the source. Applied Sciences, 2026 · DOI ↗