This study proposes an artificial intelligence-driven smart city system (AISSC) for trend detection, short-term forecasting, and decision support using continuously replayed urban data streams. The framework integrates sliding-window feature generation, adaptive statistical trend detection, forecasting, and reinforcement-learning-based policy recommendations. AISSC was evaluated as a controlled prototype using publicly available benchmark datasets replayed chronologically to emulate streaming operation; it was not deployed in a live city-wide infrastructure. Under the evaluated conditions, AISSC achieved 98.6% directional accuracy, 6.3% MAPE, and 7.8% WMAPE. The mean trend-detection latency was 0.83 s, while the mean end-to-end processing latency was 2.40 s. Dashboard information was refreshed every five minutes, representing the configured update frequency rather than processing latency. Results show the feasibility of AISSC for low-latency urban stream analysis, but more deployment-scale and longitudinal validation is needed. These results demonstrate the capability of the proposed framework to support reliable real-time trend analysis and short-term forecasting for smart city decision-making. This shows that the framework can reliably identify trends and accurately estimate demand for real-time smart city decision-making. The results demonstrate consistent performance improvements compared to representative baseline methods under identical streaming and computational constraints. The proposed AISSC facilitates real-time observation of urban dynamics and enhances short-term forecasting accuracy for informed decision-making in smart city management systems.

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.

Introduction: Manual monitoring of helmet use is impractical in dynamic, congested, and safety-critical construction environments, particularly when unmanned aerial vehicles (UAVs) capture workers from varying altitudes, viewpoints, illumination conditions, and occlusion levels. This study proposes a hybrid helmet-detection framework integrating YOLO-E and YOLOv12 to improve detection accuracy, small-object recognition, color fidelity, and open-set awareness for real-time construction safety monitoring. Methods: The proposed framework combines the language-guided open-vocabulary capabilities of YOLO-E with the real-time feature extraction and object-detection capabilities of YOLOv12. Slicing-Aided Hyper Inference (SAHI), image preprocessing, synthetic-weather augmentation, and prompt-sensitivity analysis were incorporated to improve robustness under challenging operating conditions. The framework was evaluated using a publicly balanced PPE dataset and a newly developed UAV-based helmet dataset containing 6,471 annotated helmet instances captured across different altitudes, viewing angles, lighting conditions, weather scenarios, helmet colors, and occlusion levels. We conducted ablation experiments to measure the contribution of each component of the framework. Results: The SAHI-enhanced hybrid framework increased mean average precision at the 50% IoU threshold from 89% to 97%, improved small-object recall from 0.41 to 0.80, and reduced localization error to 2.25 pixels. The proposed approach also achieved scores of 0.96 for weather robustness and 0.97 for color fidelity. Comparative experiments showed that the hybrid YOLO-E + YOLOv12 framework achieved superior precision, recall, and F1-score relative to the evaluated baseline and existing detection architectures, including Mask R-CNN, Faster R-CNN, RetinaNet, and SSD. Discussion: The results demonstrate that combining open-vocabulary recognition, closed-set detection, SAHI-based small-object enhancement, and robustness-oriented preprocessing provides a scalable solution for UAV-based helmet monitoring. The framework is particularly suitable for construction safety auditing and large-scale surveillance scenarios requiring real-time detection under variable environmental conditions, while its open-set awareness improves adaptability to previously unrecognized visual conditions.