Abstract
Reliable safety-helmet monitoring in complex industrial environments remains challenging because open-vocabulary detectors are sensitive to prompt wording and changing scene conditions. This study proposes a context-aware framework for YOLO-World and YOLOE that systematically generates color-specific prompts; selects prompts according to the camera angle, time of day, and subject distance; and combines detections using AP-weighted Weighted Box Fusion. The framework was evaluated using strictly separated calibration and test partitions of aerial IITU footage and ground-level CHV construction images. Prompt formulation had the greatest effect on detection performance, with mean AP50 ranging from 0.17 to 0.49 across prompt generation strategies. Weighted fusion produced higher point estimates than the single-best-prompt baseline in all 18 model–dataset–color configurations, with a median improvement of 0.0245 AP50, although most individual gains were not statistically resolved. A supervised YOLOv8-L model remained more accurate in-domain, whereas the open-vocabulary framework could recognize an unseen helmet color without updating detector weights. This flexibility nevertheless requires labeled target-site calibration for prompt ranking and fusion weights. The performance was weakest for long-range overhead views and visually confounded helmet colors. The proposed framework is therefore best suited as a condition-aware monitoring and triage tool, with automation restricted to validated operating regimes and human review retained for uncertain cases.