<![CDATA[Occupational safety in the oil and gas industry requires strict compliance with Personal Protective Equipment (PPE); however, manual inspection is prone to human error and lacks effectiveness. This research designs and implements a PPE Smart Station based on the YOLOv8n algorithm on an NVIDIA Jetson Nano A02 embedded system to automatically and in real-time detect the completeness of three main PPE types—helmet, coverall, and gloves—within the industrial environment of PT Husky-CNOOC Madura Limited. The system employs a state machine with PASS/DENIED output, a real-time 2D visualization of missing PPE on the worker’s anatomical regions, and Indonesian-language text alerts with automatic audio warnings. Results are reported at two levels: (i) model evaluation on a limited test batch yielded 99.0% precision and 100% recall under near-ideal capture conditions; and (ii) field system evaluation on 30 samples at a single installation point, assessed with a confusion matrix at the PASS/DENIED decision level, yielded 93.3% accuracy, 96.0% precision, 96.0% recall, 96.0% F1-measure, and a 6.7% error rate. The results indicate the system’s initial feasibility as an AI-based occupational-safety monitoring solution under the evaluated conditions, while larger-scale, multi-site validation remains necessary.]]>
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